In [2]:
%matplotlib inline

import tensorflow as tf
import numpy as np
import time
import random
import sys
import os
import tensorflow.contrib.slim as slim
from nets.vgg import vgg_16 as vgg
from nets.vgg import vgg_arg_scope
import preprocessing.imagenet_utils as imagenet_utils
import preprocessing.vgg as preprocessing
import PIL.Image as Image
import glob
from matplotlib import pyplot as plt
#from imagenet_classes import class_names
from collections import defaultdict
#from io import StringIO
from pathlib import Path
from skimage import io
from skimage.transform import resize
import math
from scipy import signal
import pandas as pd
from MASC_core import MASC_core

try:
    xrange
except NameError:
    xrange = range
In [3]:
# Helper Functions 

def load_image(img_path):
    print("Loading image")
    img = np.array(Image.open(img_path))
    #img = imresize(img, (224, 224))
    # Converting shape from [224,224,3] tp [1,224,224,3]
    #x = np.expand_dims(img, axis=0)
    # Converting RGB to BGR for VGG
    #x = x[:,:,:,::-1]
    return img

    
def image_crop(im, bbox):
    return im[bbox[0]:bbox[0]+bbox[2], bbox[1]:bbox[1]+bbox[3],:]


def gauss2D(shape,sigma):
    m,n = [(ss-1.)/2. for ss in shape]
    y,x = np.ogrid[-m:m+1,-n:n+1]
    h = np.exp( -(x*x + y*y) / (2.*sigma*sigma) )
    h[ h < np.finfo(h.dtype).eps*h.max() ] = 0
    sumh = h.sum()
    if sumh != 0:
        h /= sumh
    return h


# importing the model

model_path = './modelparams/vgg_16.ckpt'
labels_to_names, label_names = imagenet_utils.create_readable_names_for_imagenet_labels()

#read in the search displays data files
search_displays_data = pd.read_excel('object_array_displays_fixation_data/object_array_displays_data_present.xlsx')

test_images_dir = "object_array_displays_fixation_data/search_displays"


cates = ['clock' ,'crib','fan','garbage_can','microwave_oven','rug','socks','teddy_bear','tent']

imagenet_cat_dict = {
    'clock' : 409,
    'crib'  : 520,
    'fan'   : 545,
    'garbage_can'    : 412,
    'microwave_oven' : 651,
    'rug'   : 741,
    'socks' : 806,
    'teddy_bear'     : 850,
    'tent'  : 672 
}
In [4]:
# RF Organization
    
crop_sizes= np.array([[200, 200], [200, 200], [250, 250], [250, 250], [300, 300], [350, 350], [400, 400], [450, 450], [500, 500]])

row_steps = [100+100*x for x in range(5)] 
col_steps = [100+100*x for x in range(8)] 
row_shifts = np.concatenate((np.negative(row_steps[::-1]) , [0] , (row_steps)), axis=0) 
col_shifts = np.concatenate((np.negative(col_steps[::-1]) , [0] , (col_steps)), axis=0) 
print(row_shifts)


# for filename in glob.glob('final_images/*.jpg'):
#test_images_path = [glob.glob(test_images_dir + "*.jpg")][0]

#image_name = 'used_TP/COCO_val2014_000000117899.jpg'

max_num_fixs = 20
use_image_foreground_for_object_arrays = 1

use_GradCAM = 0
use_CCF = 1
if (use_CCF):
    CCF_w = np.zeros( (len(cates),512))
    for c in range(len(cates)):
        image_dir = "./object_array_displays_fixation_data/CCFs_object_array_categories/"
        w = np.load(image_dir + cates[c] + '_conv5_3_mean_map_w.npy')
        CCF_w[c,:] = w / np.max(w)

apply_IOR = 1
IOR_size = 200  # pixels
IOR_offset = math.floor(IOR_size/2)
IOR_sigma = IOR_size/2 ;
filt_IOR = gauss2D([IOR_size,IOR_size], IOR_sigma) 
filt_IOR = filt_IOR/np.max(filt_IOR)

all_fixations = np.zeros((len(search_displays_data),max_num_fixs,2))
target_presence = np.zeros((len(search_displays_data)))

for im_num in range(len(search_displays_data)):
    
    
    cat_name = search_displays_data["categorical_cue"][im_num]
    cat_name = '_'.join(cat_name.split())
    
    search_target = imagenet_cat_dict[cat_name] 
    print(cat_name + '    ' + str(search_target) )
    image_name = search_displays_data["disp_im_name"][im_num]
    
    image_path = test_images_dir + '/' + image_name
    print(image_path)

    search_im = load_image(image_path)
    
    with open('./Image_names_object_array_dislays.txt', 'a') as f1:
        f1.write(image_name + os.linesep)
    
    plt.imshow(search_im)
    plt.show()
    print(search_im.shape)
    im_size = search_im.shape
    im_h = im_size[0]
    im_w = im_size[1]

    fixation_map = np.zeros((im_h+IOR_size,im_w+IOR_size));

    im_col_fix = math.floor(im_w/2) # 493 #
    im_row_fix = math.floor(im_h/2)  # 673 #
    
    im_corner_row = 1500-math.floor(im_h/2)
    im_corner_col = 2000-math.floor(im_w/2) 

    fixations_row = np.zeros((max_num_fixs))
    fixations_col = np.zeros((max_num_fixs))
    
    fixations_row[0] = im_row_fix
    fixations_col[0] = im_col_fix

    for fix in range(1,max_num_fixs):
        
        print("Eye_movement  " + str(fix))

        row_fix = im_corner_row + im_row_fix 
        col_fix = im_corner_col + im_col_fix

        im_foreground = 255-np.mean(search_im.astype(float), axis=2)
        im_foreground_norm = im_foreground #np.multiply(im_foreground,np.divide(im_foreground, im_foreground))
        nonzero = im_foreground > 0
        im_foreground_norm[nonzero] /= im_foreground[nonzero]
    #     plt.imshow(im_foreground_norm)
    #     plt.show()

        backg = 255*np.ones((3000,4000,3))
        backg[im_corner_row:im_corner_row + im_h,im_corner_col:im_corner_col+im_w,:]=search_im;
        backg = np.uint8(backg)
#         plt.imshow(backg)
#         plt.show()

        backg_RF = backg;
        sample_size = len(col_shifts)* len(row_shifts) 
        conv5_3_feat = np.zeros((sample_size,14,14,512),dtype=float)
        #conv5_3_grads = np.zeros((sample_size,14,14,512),dtype=float)
        conv5_3_p = np.zeros((sample_size,14,14),dtype=float)

        conv5_3_p_ub = np.zeros((sample_size,14,14),dtype=float)

        crop_box_center_all = np.zeros((sample_size,2),dtype=float)
        crop_box_corner_all = np.zeros((sample_size,2),dtype=float)

        g_2 = tf.Graph()          
        with g_2.as_default():
            vgg_inputs = tf.placeholder(dtype=tf.float32, shape=[None, 224, 224, 3], name='input_image')
            with slim.arg_scope(vgg_arg_scope()):
                vgg_output, vgg_endpoints = vgg(vgg_inputs, is_training=False)

            # todo: Compute gradient layer by layer
            init_fn = slim.assign_from_checkpoint_fn(model_path, slim.get_model_variables('vgg_16'))

            sess_2 = tf.Session(graph=g_2)
            sess_2.run(tf.global_variables_initializer())
            init_fn(sess_2)
            crop_index_overall = 0
            for row_RF in range(len(row_shifts)):  # 

                sample_size = len(col_shifts)
                crop_set = np.zeros((sample_size,224,224,3),dtype=float)
                crop_index = 0

                for col_RF in range(len(col_shifts)):  #
                    row_dis = abs(math.floor((len(row_shifts)/2)) - row_RF) 
                    col_dis = abs(math.floor((len(col_shifts)/2)) - col_RF) 
                    crop_ind = max(row_dis,col_dis)

                    crop_size = crop_sizes[crop_ind]
                    #print(crop_size)
                    crop_box_center = np.array([row_fix + row_shifts[row_RF] , col_fix + col_shifts[col_RF]]) 
                    crop_box_center_all[crop_index_overall] = crop_box_center
                    #print(crop_box_center)
                    crop_corner = crop_box_center - math.floor(crop_size[0]/2) 
                    crop_box_corner_all[crop_index_overall] = crop_corner
                    #print(crop_corner)
                    im_crop = image_crop(backg_RF,np.concatenate((crop_corner, crop_size), axis = 0)) 



                    s_image = Image.fromarray(im_crop)
                    s_image = s_image.resize([224, 224], resample=Image.BICUBIC)
                    im_crop_re = np.asarray(s_image, dtype=np.float32)

                    if row_RF == math.floor(len(row_shifts)/2) and col_RF == math.floor(len(col_shifts)/2):  
                        plt.imshow(s_image)
                        plt.show()
                        time.sleep(0.5)

            #         if len(s_image.shape) < 3:
            #             print("Image is not RGB 3Dimensional data")
                    im_crop_re = preprocessing.mean_image_subtraction(im_crop_re)
                    im_crop_re = np.expand_dims(im_crop_re, axis=0)

                    crop_set[crop_index,:,:,:] = im_crop_re
                    crop_index = crop_index + 1 
                    crop_index_overall = crop_index_overall + 1
                  # crop_ind = crop_array(col_RF);

                #print('Running the session')
                vgg_all_output = sess_2.run([vgg_output, vgg_endpoints], feed_dict={vgg_inputs: crop_set})
                [current_vgg_output, current_vgg_endpoints] = vgg_all_output
                
                conv5_3_f = current_vgg_endpoints['vgg_16/conv5/conv5_3']                

                if row_RF == math.floor(len(row_shifts)/2):
                    
                    print(["Fixated area of the image and the classificatoin results"])

                    s_output = current_vgg_output[math.floor(len(col_shifts)/2)]
                    sorted_cates = np.argsort(-s_output)
                    selected_cates = sorted_cates[0:5]

                    for i, catId in enumerate(selected_cates):
                        # !!!: Notice here this is a catId+1 instead of catID
                        print('{:d}\t category: {:s}; confidence:{:.4f},\tlabelID: {:s} {:d}'.format(i, labels_to_names[
                            catId + 1], s_output[catId], label_names[catId], catId))

                if use_GradCAM:
                # apply grad-cam

                    layer_name =  'vgg_16/conv5/conv5_3'
                    conv_layer = vgg_endpoints[layer_name]  #'vgg_16/conv5/conv5_3'

                    one_hot = tf.sparse_to_dense(search_target, [1000], 1.0)
                    signal_one_hot = tf.multiply(vgg_endpoints['vgg_16/fc8'], one_hot)
                    loss = tf.reduce_mean(signal_one_hot)

                    grads = tf.gradients(loss, conv_layer)[0]
                #     # Normalizing the gradients
                    norm_grads = tf.div(grads, tf.sqrt(tf.reduce_mean(tf.square(grads))) + tf.constant(1e-5))

                    outputs, grads_val, loss_val = sess_2.run([conv_layer, norm_grads, loss], feed_dict={vgg_inputs: crop_set})
            #         output = output[0]           # [7,7,512]
            #         grads_val = grads_val[0]	 # [7,7,512]
                    outputs = np.asarray(outputs)
                    grads_val = np.asarray(grads_val)
                    #outputs_re = np.reshape(outputs,(outputs.shape[0]*outputs.shape[1]*outputs.shape[2], outputs.shape[3]))

                    all_weights = np.mean(grads_val, axis = (1, 2)) 			# [?,512]
                    all_weights = all_weights/np.max(all_weights)
                    p = np.ones(outputs.shape[0 : 3], dtype = np.float32)	# [?,14,14]
                    p_ub = np.ones(outputs.shape[0 : 3], dtype=np.float32)  # [?,14,14]    
                    #np.matmul(output_re, np.transpose(weights))

                    for j in range(outputs.shape[0]):
                        weights = all_weights[j,:]
                        output = outputs[j,:]
            #           # Taking a weighted average
                        for i, w in enumerate(weights):
                            p[j,:] += w * output[:, :, i]
                        
                        
                elif(use_CCF == 1):
                                                     
                    weights = CCF_w[cates.index(cat_name),:]
                    
                    p = np.matmul(conv5_3_f,weights)
                        
                
                p_ub = np.mean(conv5_3_f, axis=3 )

                ind_beg=17*(row_RF)
                conv5_3_feat[ind_beg:ind_beg+17,:,:,:] = current_vgg_endpoints['vgg_16/conv5/conv5_3']
                #conv5_3_grads[ind_beg:ind_beg+17,:,:,:] = grads_val
                conv5_3_p[ind_beg:ind_beg+17,:,:] = p

                conv5_3_p_ub[ind_beg:ind_beg+17,:,:] = p_ub


        if(s_output[search_target] > 5  and search_target in selected_cates[:5]):
            target_presence[im_num] = 1
            break
        
        priority_big = np.zeros((3000,4000))

        crop_box_center_all = crop_box_center_all.astype(int)
        priority_big[crop_box_center_all[:,0], crop_box_center_all[:,1]] = np.max(conv5_3_p, axis = (1, 2))

        filt = gauss2D( (200,200), 100)
        priority_big_map = signal.fftconvolve(priority_big, filt, mode='same')

        priority_map = priority_big_map[im_corner_row:im_corner_row+im_h, im_corner_col:im_corner_col + im_w]
        priority_map = priority_map/np.max(priority_map)
        #priority_map = priority_map 
        if (use_image_foreground_for_object_arrays == 1):
            priority_map = np.multiply(im_foreground_norm, priority_map)

        if (apply_IOR): 
            #fixation_map[row_im_f+IOR_offset,col_im_f+IOR_offset] = 1 
            fixation_map[im_row_fix:im_row_fix+2*IOR_offset,im_col_fix:im_col_fix+2*IOR_offset] = \
                fixation_map[im_row_fix:im_row_fix+2*IOR_offset,im_col_fix:im_col_fix+2*IOR_offset] + filt_IOR
            IOR_map = fixation_map[IOR_offset:IOR_offset+im_h,IOR_offset:IOR_offset+im_w] 
            #IOR_map = IOR_map / np.max(IOR_map)
            priority_map_fixs = priority_map - IOR_map 
            priority_map_fixs= priority_map_fixs.clip(min=0)
            
            

        print("Priority map")
        plt.imshow(priority_map_fixs, cmap='gray')  # _fixs/np.max(priority_map_fixs)
        plt.show()
        time.sleep(0.5)
        print(np.max(priority_map_fixs))
        if(np.max(priority_map_fixs) < 0.4):
            break
            
        # MASC_core is the main function 
        # input arguments: 
        #     priority_map: Priority map
        #     RETINA_PIXDEG: The number of pixels in one degree visual angle of the visual display  
        #     im_col_fix: The column for the current fixation (x)
        #     im_row_fix: The row for the current fixtion (y)
        # outputs: 
        #     col_im_m: The column for the next fixation (x)
        #     row_im_m: The row for the next fixation (x)
        #     moto_Coll_framed: The activity map in the motor layer of the SC 
        #     col_m_coll: the column coordiatne of the winning population in the SC motor map
        #     row_m_coll: the row coordiatne of the winning population in the SC motor map

        RETINA_PIXDEG = 22
        col_im_m, row_im_m, moto_Coll_framed, col_m_coll, row_m_coll = MASC_core(priority_map_fixs, RETINA_PIXDEG, im_col_fix, im_row_fix)



        plt.imshow(search_im, aspect='auto')
        plt.plot([im_col_fix, col_im_m], [im_row_fix, row_im_m], '-o', color='lawngreen')
        plt.plot(col_im_m, row_im_m, 'o',color='red')
        plt.show()

        plt.imshow(moto_Coll_framed, aspect='auto')
        plt.plot(col_m_coll, row_m_coll, 'o',color='red')
        plt.show()

        im_col_fix = col_im_m
        im_row_fix = row_im_m

        fixations_row[fix] = im_row_fix
        fixations_col[fix] = im_col_fix

    plt.imshow(search_im, aspect='auto')
    plt.plot(fixations_col[0:fix], fixations_row[0:fix], '-o', color='lawngreen')
    plt.plot(fixations_col[fix-1], fixations_row[fix-1], 'o',color='red')
    plt.savefig('./object_array_displays_fixation_data/model_scanpaths_present/' + image_name.split('.')[0] + \
                '_scanpath_'+ str(target_presence[im_num]) + '.jpg')

    plt.show()
    
    
    all_fixations[im_num,:,0] = fixations_col
    all_fixations[im_num,:,1] = fixations_row
    
    np.save('Deep-BCN_CCF_object_array_target_present.npy',all_fixations)
[-500 -400 -300 -200 -100    0  100  200  300  400  500]
crib    520
object_array_displays_fixation_data/search_displays/present_002.png
Loading image
(893, 1428, 3)
Eye_movement  1
WARNING:tensorflow:From /home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
WARNING:tensorflow:From /home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/training/saver.py:1272: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.
Instructions for updating:
Use standard file APIs to check for files with this prefix.
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: cleaver, meat cleaver, chopper; confidence:3.5071,	labelID: n03041632 499
1	 category: envelope; confidence:3.3408,	labelID: n03291819 549
2	 category: can opener, tin opener; confidence:3.0919,	labelID: n02951585 473
3	 category: corkscrew, bottle screw; confidence:3.0105,	labelID: n03109150 512
4	 category: face powder; confidence:2.9736,	labelID: n03314780 551
Priority map
0.9880708967280416
/home/ec2-user/SageMaker/BCN/MASC_core.py:16: RuntimeWarning: invalid value encountered in true_divide
  phi=np.arctan( np.divide(np.multiply(np.exp(u/RETINA_BU), np.sin(v/RETINA_BV) ), np.multiply(np.exp(u/RETINA_BU) , np.cos(v/RETINA_BV))-1.0))
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  2
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: cradle; confidence:10.2495,	labelID: n03125729 516
1	 category: crib, cot; confidence:8.4646,	labelID: n03131574 520
2	 category: hamper; confidence:8.4425,	labelID: n03482405 588
3	 category: shopping basket; confidence:7.9724,	labelID: n04204238 790
4	 category: rocking chair, rocker; confidence:7.5018,	labelID: n04099969 765
rug    741
object_array_displays_fixation_data/search_displays/present_004.png
Loading image
(893, 1428, 3)
Eye_movement  1
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: cleaver, meat cleaver, chopper; confidence:3.5071,	labelID: n03041632 499
1	 category: envelope; confidence:3.3408,	labelID: n03291819 549
2	 category: can opener, tin opener; confidence:3.0919,	labelID: n02951585 473
3	 category: corkscrew, bottle screw; confidence:3.0105,	labelID: n03109150 512
4	 category: face powder; confidence:2.9736,	labelID: n03314780 551
Priority map
0.9996714730304485
Eye_movement  2
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: drum, membranophone, tympan; confidence:8.3244,	labelID: n03249569 541
1	 category: maraca; confidence:7.4307,	labelID: n03720891 641
2	 category: hourglass; confidence:7.3573,	labelID: n03544143 604
3	 category: matchstick; confidence:7.2963,	labelID: n03729826 644
4	 category: golf ball; confidence:7.1509,	labelID: n03445777 574
Priority map
0.48784334969918536
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  3
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: abacus; confidence:6.7108,	labelID: n02666196 398
1	 category: mousetrap; confidence:5.8568,	labelID: n03794056 674
2	 category: wall clock; confidence:5.7306,	labelID: n04548280 892
3	 category: analog clock; confidence:5.6466,	labelID: n02708093 409
4	 category: envelope; confidence:5.5204,	labelID: n03291819 549
Priority map
0.8905017166559845
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  4
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: starfish, sea star; confidence:5.9310,	labelID: n02317335 327
1	 category: conch; confidence:5.3104,	labelID: n01943899 112
2	 category: necklace; confidence:5.2955,	labelID: n03814906 679
3	 category: stole; confidence:5.0409,	labelID: n04325704 824
4	 category: gown; confidence:4.7419,	labelID: n03450230 578
Priority map
0.36245593147535055
teddy_bear    850
object_array_displays_fixation_data/search_displays/present_008.png
Loading image
(893, 1428, 3)
Eye_movement  1
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: cleaver, meat cleaver, chopper; confidence:3.5071,	labelID: n03041632 499
1	 category: envelope; confidence:3.3408,	labelID: n03291819 549
2	 category: can opener, tin opener; confidence:3.0919,	labelID: n02951585 473
3	 category: corkscrew, bottle screw; confidence:3.0105,	labelID: n03109150 512
4	 category: face powder; confidence:2.9736,	labelID: n03314780 551
Priority map
0.9271447058897001
Eye_movement  2
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: piggy bank, penny bank; confidence:6.4464,	labelID: n03935335 719
1	 category: maraca; confidence:5.9559,	labelID: n03720891 641
2	 category: web site, website, internet site, site; confidence:5.8000,	labelID: n06359193 916
3	 category: Chihuahua; confidence:5.3733,	labelID: n02085620 151
4	 category: triceratops; confidence:5.2883,	labelID: n01704323 51
Priority map
0.9281786366890032
Eye_movement  3
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: waffle iron; confidence:8.4171,	labelID: n04542943 891
1	 category: toaster; confidence:7.8962,	labelID: n04442312 859
2	 category: nail; confidence:7.3546,	labelID: n03804744 677
3	 category: spatula; confidence:7.2601,	labelID: n04270147 813
4	 category: hair slide; confidence:6.6351,	labelID: n03476684 584
Priority map
0.8782268464582279
Eye_movement  4
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: overskirt; confidence:10.8342,	labelID: n03866082 689
1	 category: hoopskirt, crinoline; confidence:9.2365,	labelID: n03534580 601
2	 category: poncho; confidence:9.1862,	labelID: n03980874 735
3	 category: wool, woolen, woollen; confidence:9.1611,	labelID: n04599235 911
4	 category: stole; confidence:9.0745,	labelID: n04325704 824
Priority map
0.7804034813022492
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  5
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: spotlight, spot; confidence:10.2650,	labelID: n04286575 818
1	 category: table lamp; confidence:8.8952,	labelID: n04380533 846
2	 category: soap dispenser; confidence:8.5504,	labelID: n04254120 804
3	 category: goblet; confidence:8.2267,	labelID: n03443371 572
4	 category: scale, weighing machine; confidence:7.7260,	labelID: n04141975 778
Priority map
0.6308717537216944
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  6
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: nail; confidence:6.8451,	labelID: n03804744 677
1	 category: can opener, tin opener; confidence:6.8408,	labelID: n02951585 473
2	 category: strainer; confidence:6.7516,	labelID: n04332243 828
3	 category: face powder; confidence:6.2643,	labelID: n03314780 551
4	 category: hook, claw; confidence:6.2545,	labelID: n03532672 600
Priority map
0.8168635078381105
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  7
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: forklift; confidence:10.2106,	labelID: n03384352 561
1	 category: tow truck, tow car, wrecker; confidence:9.9794,	labelID: n04461696 864
2	 category: harvester, reaper; confidence:9.9541,	labelID: n03496892 595
3	 category: thresher, thrasher, threshing machine; confidence:9.3324,	labelID: n04428191 856
4	 category: lawn mower, mower; confidence:9.1941,	labelID: n03649909 621
Priority map
0.6709130076532698
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  8
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: stretcher; confidence:8.7309,	labelID: n04336792 830
1	 category: racer, race car, racing car; confidence:7.4727,	labelID: n04037443 751
2	 category: vacuum, vacuum cleaner; confidence:7.3778,	labelID: n04517823 882
3	 category: cab, hack, taxi, taxicab; confidence:7.3489,	labelID: n02930766 468
4	 category: lawn mower, mower; confidence:6.9878,	labelID: n03649909 621
Priority map
0.7988301598977396
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  9
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: pencil sharpener; confidence:7.2269,	labelID: n03908714 710
1	 category: switch, electric switch, electrical switch; confidence:6.5904,	labelID: n04372370 844
2	 category: lighter, light, igniter, ignitor; confidence:6.3135,	labelID: n03666591 626
3	 category: purse; confidence:6.2247,	labelID: n04026417 748
4	 category: hook, claw; confidence:5.9762,	labelID: n03532672 600
Priority map
0.9885368815829235
Eye_movement  10
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: hair slide; confidence:8.7529,	labelID: n03476684 584
1	 category: hook, claw; confidence:8.1799,	labelID: n03532672 600
2	 category: nail; confidence:7.2427,	labelID: n03804744 677
3	 category: letter opener, paper knife, paperknife; confidence:6.8011,	labelID: n03658185 623
4	 category: scabbard; confidence:6.6340,	labelID: n04141327 777
Priority map
0.9045824652036869
Eye_movement  11
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: teddy, teddy bear; confidence:9.3627,	labelID: n04399382 850
1	 category: ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus; confidence:6.9480,	labelID: n02134084 296
2	 category: nipple; confidence:6.5347,	labelID: n03825788 680
3	 category: rubber eraser, rubber, pencil eraser; confidence:5.3641,	labelID: n04116512 767
4	 category: golden retriever; confidence:4.9055,	labelID: n02099601 207
rug    741
object_array_displays_fixation_data/search_displays/present_009.png
Loading image
(893, 1428, 3)
Eye_movement  1
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: cleaver, meat cleaver, chopper; confidence:3.5071,	labelID: n03041632 499
1	 category: envelope; confidence:3.3408,	labelID: n03291819 549
2	 category: can opener, tin opener; confidence:3.0919,	labelID: n02951585 473
3	 category: corkscrew, bottle screw; confidence:3.0105,	labelID: n03109150 512
4	 category: face powder; confidence:2.9736,	labelID: n03314780 551
Priority map
1.0
Eye_movement  2
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: prayer rug, prayer mat; confidence:13.1029,	labelID: n03998194 741
1	 category: doormat, welcome mat; confidence:9.1099,	labelID: n03223299 539
2	 category: Windsor tie; confidence:7.9238,	labelID: n04591157 906
3	 category: mousetrap; confidence:7.6613,	labelID: n03794056 674
4	 category: wallet, billfold, notecase, pocketbook; confidence:7.5252,	labelID: n04548362 893
microwave_oven    651
object_array_displays_fixation_data/search_displays/present_017.png
Loading image
(893, 1428, 3)
Eye_movement  1
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: cleaver, meat cleaver, chopper; confidence:3.5071,	labelID: n03041632 499
1	 category: envelope; confidence:3.3408,	labelID: n03291819 549
2	 category: can opener, tin opener; confidence:3.0919,	labelID: n02951585 473
3	 category: corkscrew, bottle screw; confidence:3.0105,	labelID: n03109150 512
4	 category: face powder; confidence:2.9736,	labelID: n03314780 551
Priority map
0.8823422303089314
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  2
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: gong, tam-tam; confidence:8.0211,	labelID: n03447721 577
1	 category: shield, buckler; confidence:6.9990,	labelID: n04192698 787
2	 category: bolo tie, bolo, bola tie, bola; confidence:6.6695,	labelID: n02865351 451
3	 category: chime, bell, gong; confidence:6.6431,	labelID: n03017168 494
4	 category: nematode, nematode worm, roundworm; confidence:6.5554,	labelID: n01930112 111
Priority map
0.9796845269236731
Eye_movement  3
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: barracouta, snoek; confidence:7.3227,	labelID: n02514041 389
1	 category: coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch; confidence:6.3641,	labelID: n02536864 391
2	 category: gar, garfish, garpike, billfish, Lepisosteus osseus; confidence:5.9504,	labelID: n02641379 395
3	 category: redshank, Tringa totanus; confidence:5.6798,	labelID: n02028035 141
4	 category: reel; confidence:5.5985,	labelID: n04067472 758
Priority map
1.0
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  4
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: long-horned beetle, longicorn, longicorn beetle; confidence:7.5602,	labelID: n02168699 303
1	 category: hair slide; confidence:6.8112,	labelID: n03476684 584
2	 category: necklace; confidence:6.6349,	labelID: n03814906 679
3	 category: ground beetle, carabid beetle; confidence:6.4848,	labelID: n02167151 302
4	 category: tiger beetle; confidence:6.0831,	labelID: n02165105 300
Priority map
0.9857838359282914
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  5
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: radio, wireless; confidence:8.2526,	labelID: n04041544 754
1	 category: loudspeaker, speaker, speaker unit, loudspeaker system, speaker system; confidence:7.4231,	labelID: n03691459 632
2	 category: digital clock; confidence:7.2648,	labelID: n03196217 530
3	 category: microwave, microwave oven; confidence:7.1087,	labelID: n03761084 651
4	 category: CD player; confidence:6.9892,	labelID: n02988304 485
tent    672
object_array_displays_fixation_data/search_displays/present_019.png
Loading image
(893, 1428, 3)
Eye_movement  1
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: cleaver, meat cleaver, chopper; confidence:3.5071,	labelID: n03041632 499
1	 category: envelope; confidence:3.3408,	labelID: n03291819 549
2	 category: can opener, tin opener; confidence:3.0919,	labelID: n02951585 473
3	 category: corkscrew, bottle screw; confidence:3.0105,	labelID: n03109150 512
4	 category: face powder; confidence:2.9736,	labelID: n03314780 551
Priority map
0.7548733351425205
Eye_movement  2
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: coffeepot; confidence:6.4726,	labelID: n03063689 505
1	 category: candle, taper, wax light; confidence:6.4585,	labelID: n02948072 470
2	 category: teapot; confidence:6.2968,	labelID: n04398044 849
3	 category: iron, smoothing iron; confidence:6.2665,	labelID: n03584829 606
4	 category: espresso maker; confidence:6.1970,	labelID: n03297495 550
Priority map
0.97357757946193
Eye_movement  3
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: convertible; confidence:6.7719,	labelID: n03100240 511
1	 category: lawn mower, mower; confidence:6.3880,	labelID: n03649909 621
2	 category: cab, hack, taxi, taxicab; confidence:5.8413,	labelID: n02930766 468
3	 category: spotlight, spot; confidence:5.7124,	labelID: n04286575 818
4	 category: sports car, sport car; confidence:5.6224,	labelID: n04285008 817
Priority map
0.9849704611843509
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  4
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: soccer ball; confidence:15.0062,	labelID: n04254680 805
1	 category: golf ball; confidence:9.0362,	labelID: n03445777 574
2	 category: volleyball; confidence:9.0299,	labelID: n04540053 890
3	 category: teddy, teddy bear; confidence:7.3530,	labelID: n04399382 850
4	 category: ping-pong ball; confidence:7.1726,	labelID: n03942813 722
Priority map
0.9633885922924205
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  5
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: pencil sharpener; confidence:8.5380,	labelID: n03908714 710
1	 category: thimble; confidence:8.4998,	labelID: n04423845 855
2	 category: whistle; confidence:8.1799,	labelID: n04579432 902
3	 category: binoculars, field glasses, opera glasses; confidence:7.3527,	labelID: n02841315 447
4	 category: buckle; confidence:7.2986,	labelID: n02910353 464
Priority map
0.9889529181996144
Eye_movement  6
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: birdhouse; confidence:6.4615,	labelID: n02843684 448
1	 category: pencil sharpener; confidence:6.0930,	labelID: n03908714 710
2	 category: thimble; confidence:5.9830,	labelID: n04423845 855
3	 category: chime, bell, gong; confidence:5.8665,	labelID: n03017168 494
4	 category: sax, saxophone; confidence:5.4906,	labelID: n04141076 776
Priority map
0.9589974520733543
Eye_movement  7
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: sleeping bag; confidence:8.0552,	labelID: n04235860 797
1	 category: sandal; confidence:7.6756,	labelID: n04133789 774
2	 category: pencil sharpener; confidence:7.0419,	labelID: n03908714 710
3	 category: mountain tent; confidence:6.8922,	labelID: n03792972 672
4	 category: parachute, chute; confidence:6.4068,	labelID: n03888257 701
garbage_can    412
object_array_displays_fixation_data/search_displays/present_020.png
Loading image
(893, 1428, 3)
Eye_movement  1
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: cleaver, meat cleaver, chopper; confidence:3.5071,	labelID: n03041632 499
1	 category: envelope; confidence:3.3408,	labelID: n03291819 549
2	 category: can opener, tin opener; confidence:3.0919,	labelID: n02951585 473
3	 category: corkscrew, bottle screw; confidence:3.0105,	labelID: n03109150 512
4	 category: face powder; confidence:2.9736,	labelID: n03314780 551
Priority map
0.9259306376312012
Eye_movement  2
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: hook, claw; confidence:6.1823,	labelID: n03532672 600
1	 category: dumbbell; confidence:6.0476,	labelID: n03255030 543
2	 category: spindle; confidence:5.6755,	labelID: n04277352 816
3	 category: barrow, garden cart, lawn cart, wheelbarrow; confidence:5.4580,	labelID: n02797295 428
4	 category: swab, swob, mop; confidence:5.4545,	labelID: n04367480 840
Priority map
0.9341559382711657
Eye_movement  3
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: tractor; confidence:15.5949,	labelID: n04465501 866
1	 category: harvester, reaper; confidence:14.6976,	labelID: n03496892 595
2	 category: plow, plough; confidence:13.6325,	labelID: n03967562 730
3	 category: thresher, thrasher, threshing machine; confidence:13.0853,	labelID: n04428191 856
4	 category: forklift; confidence:13.0175,	labelID: n03384352 561
Priority map
0.929345682016309
Eye_movement  4
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: eggnog; confidence:8.9769,	labelID: n07932039 969
1	 category: beer glass; confidence:8.7068,	labelID: n02823750 441
2	 category: cup; confidence:7.9470,	labelID: n07930864 968
3	 category: coffee mug; confidence:7.3666,	labelID: n03063599 504
4	 category: pitcher, ewer; confidence:6.8239,	labelID: n03950228 725
Priority map
0.8733200490752298
Eye_movement  5
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: doormat, welcome mat; confidence:8.3606,	labelID: n03223299 539
1	 category: jigsaw puzzle; confidence:8.0691,	labelID: n03598930 611
2	 category: envelope; confidence:7.1757,	labelID: n03291819 549
3	 category: kimono; confidence:7.0646,	labelID: n03617480 614
4	 category: bow tie, bow-tie, bowtie; confidence:6.9175,	labelID: n02883205 457
Priority map
0.960916671599949
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  6
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: harmonica, mouth organ, harp, mouth harp; confidence:11.4920,	labelID: n03494278 593
1	 category: cassette; confidence:10.2200,	labelID: n02978881 481
2	 category: wallet, billfold, notecase, pocketbook; confidence:9.1117,	labelID: n04548362 893
3	 category: modem; confidence:8.7988,	labelID: n03777754 662
4	 category: binder, ring-binder; confidence:8.4809,	labelID: n02840245 446
Priority map
0.8775448179658568
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  7
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: stretcher; confidence:7.1008,	labelID: n04336792 830
1	 category: convertible; confidence:6.8190,	labelID: n03100240 511
2	 category: beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon; confidence:6.1344,	labelID: n02814533 436
3	 category: scale, weighing machine; confidence:6.0712,	labelID: n04141975 778
4	 category: crane; confidence:6.0418,	labelID: n03126707 517
Priority map
0.9385302433266046
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  8
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: shopping basket; confidence:7.4360,	labelID: n04204238 790
1	 category: bucket, pail; confidence:7.3188,	labelID: n02909870 463
2	 category: ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin; confidence:6.8042,	labelID: n02747177 412
3	 category: strainer; confidence:6.2051,	labelID: n04332243 828
4	 category: desk; confidence:5.8766,	labelID: n03179701 526
microwave_oven    651
object_array_displays_fixation_data/search_displays/present_021.png
Loading image
(893, 1428, 3)
Eye_movement  1
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: cleaver, meat cleaver, chopper; confidence:3.5071,	labelID: n03041632 499
1	 category: envelope; confidence:3.3408,	labelID: n03291819 549
2	 category: can opener, tin opener; confidence:3.0919,	labelID: n02951585 473
3	 category: corkscrew, bottle screw; confidence:3.0105,	labelID: n03109150 512
4	 category: face powder; confidence:2.9736,	labelID: n03314780 551
Priority map
0.766899057394879
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  2
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: radio, wireless; confidence:9.2044,	labelID: n04041544 754
1	 category: CD player; confidence:8.7894,	labelID: n02988304 485
2	 category: digital clock; confidence:8.3107,	labelID: n03196217 530
3	 category: tape player; confidence:8.2176,	labelID: n04392985 848
4	 category: cellular telephone, cellular phone, cellphone, cell, mobile phone; confidence:8.1514,	labelID: n02992529 487
Priority map
0.9354650525819328
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  3
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: radio, wireless; confidence:8.8984,	labelID: n04041544 754
1	 category: digital clock; confidence:8.2762,	labelID: n03196217 530
2	 category: loudspeaker, speaker, speaker unit, loudspeaker system, speaker system; confidence:7.9632,	labelID: n03691459 632
3	 category: switch, electric switch, electrical switch; confidence:7.8664,	labelID: n04372370 844
4	 category: safe; confidence:7.7586,	labelID: n04125021 771
Priority map
0.2556930672454184
garbage_can    412
object_array_displays_fixation_data/search_displays/present_022.png
Loading image
(893, 1428, 3)
Eye_movement  1
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: cleaver, meat cleaver, chopper; confidence:3.5071,	labelID: n03041632 499
1	 category: envelope; confidence:3.3408,	labelID: n03291819 549
2	 category: can opener, tin opener; confidence:3.0919,	labelID: n02951585 473
3	 category: corkscrew, bottle screw; confidence:3.0105,	labelID: n03109150 512
4	 category: face powder; confidence:2.9736,	labelID: n03314780 551
Priority map
0.9755914994943374
Eye_movement  2
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: bucket, pail; confidence:11.1601,	labelID: n02909870 463
1	 category: ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin; confidence:10.7405,	labelID: n02747177 412
2	 category: water jug; confidence:9.8039,	labelID: n04560804 899
3	 category: milk can; confidence:9.2134,	labelID: n03764736 653
4	 category: barrel, cask; confidence:9.1478,	labelID: n02795169 427
socks    806
object_array_displays_fixation_data/search_displays/present_025.png
Loading image
(893, 1428, 3)
Eye_movement  1
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: cleaver, meat cleaver, chopper; confidence:3.5071,	labelID: n03041632 499
1	 category: envelope; confidence:3.3408,	labelID: n03291819 549
2	 category: can opener, tin opener; confidence:3.0919,	labelID: n02951585 473
3	 category: corkscrew, bottle screw; confidence:3.0105,	labelID: n03109150 512
4	 category: face powder; confidence:2.9736,	labelID: n03314780 551
Priority map
0.8922753619365085
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  2
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: pitcher, ewer; confidence:11.4457,	labelID: n03950228 725
1	 category: teapot; confidence:10.4415,	labelID: n04398044 849
2	 category: water jug; confidence:9.8470,	labelID: n04560804 899
3	 category: coffeepot; confidence:8.7879,	labelID: n03063689 505
4	 category: vase; confidence:8.5663,	labelID: n04522168 883
Priority map
0.9849852153729263
Eye_movement  3
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: sock; confidence:9.2743,	labelID: n04254777 806
1	 category: Christmas stocking; confidence:9.0021,	labelID: n03026506 496
2	 category: diaper, nappy, napkin; confidence:7.4135,	labelID: n03188531 529
3	 category: hair slide; confidence:7.2235,	labelID: n03476684 584
4	 category: Band Aid; confidence:7.1587,	labelID: n02786058 419
clock    409
object_array_displays_fixation_data/search_displays/present_028.png
Loading image
(893, 1428, 3)
Eye_movement  1
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: cleaver, meat cleaver, chopper; confidence:3.5071,	labelID: n03041632 499
1	 category: envelope; confidence:3.3408,	labelID: n03291819 549
2	 category: can opener, tin opener; confidence:3.0919,	labelID: n02951585 473
3	 category: corkscrew, bottle screw; confidence:3.0105,	labelID: n03109150 512
4	 category: face powder; confidence:2.9736,	labelID: n03314780 551
Priority map
1.0
Eye_movement  2
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: analog clock; confidence:9.9432,	labelID: n02708093 409
1	 category: digital clock; confidence:9.6586,	labelID: n03196217 530
2	 category: wall clock; confidence:9.5863,	labelID: n04548280 892
3	 category: switch, electric switch, electrical switch; confidence:8.9972,	labelID: n04372370 844
4	 category: lighter, light, igniter, ignitor; confidence:8.7715,	labelID: n03666591 626
teddy_bear    850
object_array_displays_fixation_data/search_displays/present_031.png
Loading image
(893, 1428, 3)
Eye_movement  1
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: buckeye, horse chestnut, conker; confidence:3.6423,	labelID: n12768682 990
1	 category: nail; confidence:3.3187,	labelID: n03804744 677
2	 category: tick; confidence:3.1214,	labelID: n01776313 78
3	 category: acorn; confidence:2.9743,	labelID: n12267677 988
4	 category: ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle; confidence:2.8912,	labelID: n02165456 301
Priority map
0.9999814826844605
Eye_movement  2
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: saltshaker, salt shaker; confidence:8.2766,	labelID: n04131690 773
1	 category: whiskey jug; confidence:8.0974,	labelID: n04579145 901
2	 category: piggy bank, penny bank; confidence:7.3973,	labelID: n03935335 719
3	 category: earthstar; confidence:6.7530,	labelID: n13044778 995
4	 category: toaster; confidence:6.5034,	labelID: n04442312 859
Priority map
1.0
Eye_movement  3
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: lighter, light, igniter, ignitor; confidence:7.7475,	labelID: n03666591 626
1	 category: digital clock; confidence:6.8223,	labelID: n03196217 530
2	 category: water bottle; confidence:6.7314,	labelID: n04557648 898
3	 category: pencil sharpener; confidence:6.1557,	labelID: n03908714 710
4	 category: modem; confidence:6.1518,	labelID: n03777754 662
Priority map
0.9812338417131545
Eye_movement  4
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: teddy, teddy bear; confidence:11.7393,	labelID: n04399382 850
1	 category: hamper; confidence:9.3045,	labelID: n03482405 588
2	 category: pretzel; confidence:7.8778,	labelID: n07695742 932
3	 category: pencil box, pencil case; confidence:7.7066,	labelID: n03908618 709
4	 category: hair slide; confidence:7.6096,	labelID: n03476684 584
garbage_can    412
object_array_displays_fixation_data/search_displays/present_033.png
Loading image
(893, 1428, 3)
Eye_movement  1
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: cleaver, meat cleaver, chopper; confidence:3.5071,	labelID: n03041632 499
1	 category: envelope; confidence:3.3408,	labelID: n03291819 549
2	 category: can opener, tin opener; confidence:3.0919,	labelID: n02951585 473
3	 category: corkscrew, bottle screw; confidence:3.0105,	labelID: n03109150 512
4	 category: face powder; confidence:2.9736,	labelID: n03314780 551
Priority map
0.6437809870647063
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  2
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: hair slide; confidence:9.2887,	labelID: n03476684 584
1	 category: knot; confidence:7.3505,	labelID: n03627232 616
2	 category: mitten; confidence:6.9741,	labelID: n03775071 658
3	 category: strawberry; confidence:6.5596,	labelID: n07745940 949
4	 category: starfish, sea star; confidence:6.3381,	labelID: n02317335 327
Priority map
0.8082168573096012
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  3
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: screwdriver; confidence:10.3828,	labelID: n04154565 784
1	 category: broom; confidence:8.6858,	labelID: n02906734 462
2	 category: plunger, plumber's helper; confidence:8.3617,	labelID: n03970156 731
3	 category: power drill; confidence:8.2152,	labelID: n03995372 740
4	 category: screw; confidence:7.9652,	labelID: n04153751 783
Priority map
0.8684441168229527
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  4
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: screwdriver; confidence:8.1150,	labelID: n04154565 784
1	 category: swab, swob, mop; confidence:7.6127,	labelID: n04367480 840
2	 category: power drill; confidence:7.3741,	labelID: n03995372 740
3	 category: plunger, plumber's helper; confidence:7.2781,	labelID: n03970156 731
4	 category: broom; confidence:7.0227,	labelID: n02906734 462
Priority map
0.6316580438477911
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  5
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: magnetic compass; confidence:8.8693,	labelID: n03706229 635
1	 category: loupe, jeweler's loupe; confidence:8.5857,	labelID: n03692522 633
2	 category: switch, electric switch, electrical switch; confidence:8.0379,	labelID: n04372370 844
3	 category: joystick; confidence:7.6734,	labelID: n03602883 613
4	 category: cassette; confidence:7.4105,	labelID: n02978881 481
Priority map
0.1018629635880417
crib    520
object_array_displays_fixation_data/search_displays/present_036.png
Loading image
(893, 1428, 3)
Eye_movement  1
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: tennis ball; confidence:7.1445,	labelID: n04409515 852
1	 category: Granny Smith; confidence:5.5327,	labelID: n07742313 948
2	 category: maraca; confidence:4.8815,	labelID: n03720891 641
3	 category: croquet ball; confidence:4.5639,	labelID: n03134739 522
4	 category: golf ball; confidence:4.3461,	labelID: n03445777 574
Priority map
0.9199209146960133
Eye_movement  2
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: chest; confidence:9.0622,	labelID: n03014705 492
1	 category: crate; confidence:7.8675,	labelID: n03127925 519
2	 category: hamper; confidence:7.7059,	labelID: n03482405 588
3	 category: rocking chair, rocker; confidence:7.4363,	labelID: n04099969 765
4	 category: throne; confidence:7.3492,	labelID: n04429376 857
Priority map
0.942529622092711
Eye_movement  3
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: table lamp; confidence:8.1489,	labelID: n04380533 846
1	 category: lampshade, lamp shade; confidence:7.3002,	labelID: n03637318 619
2	 category: pedestal, plinth, footstall; confidence:6.2862,	labelID: n03903868 708
3	 category: spotlight, spot; confidence:5.4229,	labelID: n04286575 818
4	 category: candle, taper, wax light; confidence:5.3056,	labelID: n02948072 470
Priority map
0.8178467596994318
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  4
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: paper towel; confidence:6.3096,	labelID: n03887697 700
1	 category: bib; confidence:6.2373,	labelID: n02834397 443
2	 category: nipple; confidence:5.9707,	labelID: n03825788 680
3	 category: goose; confidence:5.8075,	labelID: n01855672 99
4	 category: pill bottle; confidence:5.6527,	labelID: n03937543 720
Priority map
0.7791332084711603
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  5
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: airship, dirigible; confidence:7.0312,	labelID: n02692877 405
1	 category: crane; confidence:6.0314,	labelID: n03126707 517
2	 category: warplane, military plane; confidence:5.3369,	labelID: n04552348 895
3	 category: hook, claw; confidence:5.1805,	labelID: n03532672 600
4	 category: parachute, chute; confidence:5.1147,	labelID: n03888257 701
Priority map
1.0
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  6
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: cradle; confidence:11.7108,	labelID: n03125729 516
1	 category: crib, cot; confidence:11.6029,	labelID: n03131574 520
2	 category: hamper; confidence:11.4046,	labelID: n03482405 588
3	 category: plate rack; confidence:11.0596,	labelID: n03961711 729
4	 category: bassinet; confidence:9.0587,	labelID: n02804414 431
tent    672
object_array_displays_fixation_data/search_displays/present_040.png
Loading image
(893, 1428, 3)
Eye_movement  1
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: cleaver, meat cleaver, chopper; confidence:3.5071,	labelID: n03041632 499
1	 category: envelope; confidence:3.3408,	labelID: n03291819 549
2	 category: can opener, tin opener; confidence:3.0919,	labelID: n02951585 473
3	 category: corkscrew, bottle screw; confidence:3.0105,	labelID: n03109150 512
4	 category: face powder; confidence:2.9736,	labelID: n03314780 551
Priority map
1.0
Eye_movement  2
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: daisy; confidence:7.9200,	labelID: n11939491 985
1	 category: vase; confidence:6.2498,	labelID: n04522168 883
2	 category: hair slide; confidence:5.5009,	labelID: n03476684 584
3	 category: strawberry; confidence:5.3087,	labelID: n07745940 949
4	 category: orange; confidence:5.0985,	labelID: n07747607 950
Priority map
0.9743606034040965
Eye_movement  3
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: umbrella; confidence:8.5281,	labelID: n04507155 879
1	 category: hook, claw; confidence:8.4631,	labelID: n03532672 600
2	 category: vacuum, vacuum cleaner; confidence:7.0852,	labelID: n04517823 882
3	 category: padlock; confidence:7.0784,	labelID: n03874599 695
4	 category: iron, smoothing iron; confidence:6.9568,	labelID: n03584829 606
Priority map
0.7763795610691248
Eye_movement  4
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: hook, claw; confidence:9.7526,	labelID: n03532672 600
1	 category: hammer; confidence:8.1206,	labelID: n03481172 587
2	 category: whistle; confidence:7.8928,	labelID: n04579432 902
3	 category: magnetic compass; confidence:7.5328,	labelID: n03706229 635
4	 category: padlock; confidence:7.3907,	labelID: n03874599 695
Priority map
0.6869908394465588
Eye_movement  5
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: guacamole; confidence:11.4823,	labelID: n07583066 924
1	 category: mortar; confidence:10.7686,	labelID: n03786901 666
2	 category: pot, flowerpot; confidence:10.6219,	labelID: n03991062 738
3	 category: caldron, cauldron; confidence:8.5315,	labelID: n02939185 469
4	 category: bucket, pail; confidence:8.0862,	labelID: n02909870 463
Priority map
0.7848126565893949
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  6
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: coil, spiral, volute, whorl, helix; confidence:9.6497,	labelID: n03065424 506
1	 category: knot; confidence:8.7586,	labelID: n03627232 616
2	 category: nematode, nematode worm, roundworm; confidence:8.1098,	labelID: n01930112 111
3	 category: hair slide; confidence:7.9840,	labelID: n03476684 584
4	 category: microphone, mike; confidence:7.1975,	labelID: n03759954 650
Priority map
0.6840938909375198
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  7
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: teapot; confidence:8.7147,	labelID: n04398044 849
1	 category: pitcher, ewer; confidence:8.3361,	labelID: n03950228 725
2	 category: whiskey jug; confidence:8.0891,	labelID: n04579145 901
3	 category: water jug; confidence:7.8429,	labelID: n04560804 899
4	 category: coffeepot; confidence:6.8413,	labelID: n03063689 505
Priority map
0.907694682392777
Eye_movement  8
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: pretzel; confidence:7.0444,	labelID: n07695742 932
1	 category: strainer; confidence:6.9878,	labelID: n04332243 828
2	 category: waffle iron; confidence:6.3662,	labelID: n04542943 891
3	 category: scale, weighing machine; confidence:6.0601,	labelID: n04141975 778
4	 category: pomegranate; confidence:5.8448,	labelID: n07768694 957
Priority map
0.7687892908674537
Eye_movement  9
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: strainer; confidence:7.6056,	labelID: n04332243 828
1	 category: loupe, jeweler's loupe; confidence:7.5330,	labelID: n03692522 633
2	 category: espresso; confidence:6.7926,	labelID: n07920052 967
3	 category: cup; confidence:6.7556,	labelID: n07930864 968
4	 category: toaster; confidence:6.7465,	labelID: n04442312 859
Priority map
0.8405201250021708
Eye_movement  10
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: Band Aid; confidence:11.6869,	labelID: n02786058 419
1	 category: rubber eraser, rubber, pencil eraser; confidence:11.4440,	labelID: n04116512 767
2	 category: honeycomb; confidence:8.5806,	labelID: n03530642 599
3	 category: pencil sharpener; confidence:8.0600,	labelID: n03908714 710
4	 category: ice lolly, lolly, lollipop, popsicle; confidence:7.9541,	labelID: n07615774 929
Priority map
0.9840571251306496
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  11
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: tench, Tinca tinca; confidence:11.3464,	labelID: n01440764 0
1	 category: coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch; confidence:10.8537,	labelID: n02536864 391
2	 category: barracouta, snoek; confidence:10.7654,	labelID: n02514041 389
3	 category: reel; confidence:10.7037,	labelID: n04067472 758
4	 category: gar, garfish, garpike, billfish, Lepisosteus osseus; confidence:9.0939,	labelID: n02641379 395
Priority map
0.9973581074580251
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  12
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: crane; confidence:11.6019,	labelID: n03126707 517
1	 category: harvester, reaper; confidence:10.5739,	labelID: n03496892 595
2	 category: tractor; confidence:10.2266,	labelID: n04465501 866
3	 category: forklift; confidence:9.9036,	labelID: n03384352 561
4	 category: snowplow, snowplough; confidence:9.7964,	labelID: n04252225 803
Priority map
0.808552818950842
Eye_movement  13
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: dowitcher; confidence:8.3102,	labelID: n02033041 142
1	 category: bittern; confidence:7.0035,	labelID: n02011460 133
2	 category: limpkin, Aramus pictus; confidence:6.8406,	labelID: n02013706 135
3	 category: kite; confidence:6.6895,	labelID: n01608432 21
4	 category: prairie chicken, prairie grouse, prairie fowl; confidence:6.6292,	labelID: n01798484 83
Priority map
0.9778738246261942
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  14
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: buckle; confidence:5.6312,	labelID: n02910353 464
1	 category: lighter, light, igniter, ignitor; confidence:5.1254,	labelID: n03666591 626
2	 category: mousetrap; confidence:4.8068,	labelID: n03794056 674
3	 category: throne; confidence:4.6940,	labelID: n04429376 857
4	 category: hook, claw; confidence:4.5867,	labelID: n03532672 600
Priority map
0.8011337738579614
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  15
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: sewing machine; confidence:8.5804,	labelID: n04179913 786
1	 category: iron, smoothing iron; confidence:7.3909,	labelID: n03584829 606
2	 category: guillotine; confidence:6.9712,	labelID: n03467068 583
3	 category: nail; confidence:6.7299,	labelID: n03804744 677
4	 category: pencil sharpener; confidence:6.6827,	labelID: n03908714 710
Priority map
0.9259322890532544
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  16
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: starfish, sea star; confidence:7.6679,	labelID: n02317335 327
1	 category: corn; confidence:7.2304,	labelID: n12144580 987
2	 category: nail; confidence:6.2639,	labelID: n03804744 677
3	 category: screw; confidence:6.0059,	labelID: n04153751 783
4	 category: ear, spike, capitulum; confidence:5.8945,	labelID: n13133613 998
Priority map
0.8337154426013823
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  17
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: bucket, pail; confidence:9.0141,	labelID: n02909870 463
1	 category: hook, claw; confidence:8.8271,	labelID: n03532672 600
2	 category: tricycle, trike, velocipede; confidence:8.8179,	labelID: n04482393 870
3	 category: scale, weighing machine; confidence:8.7687,	labelID: n04141975 778
4	 category: measuring cup; confidence:8.4133,	labelID: n03733805 647
Priority map
0.9084516182229849
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  18
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: strainer; confidence:9.0809,	labelID: n04332243 828
1	 category: mixing bowl; confidence:8.6338,	labelID: n03775546 659
2	 category: frying pan, frypan, skillet; confidence:8.1830,	labelID: n03400231 567
3	 category: mortar; confidence:7.7865,	labelID: n03786901 666
4	 category: cup; confidence:7.3602,	labelID: n07930864 968
Priority map
0.8970817535309596
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  19
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: envelope; confidence:4.1877,	labelID: n03291819 549
1	 category: banana; confidence:3.5852,	labelID: n07753592 954
2	 category: corn; confidence:3.2842,	labelID: n12144580 987
3	 category: hamper; confidence:3.1833,	labelID: n03482405 588
4	 category: web site, website, internet site, site; confidence:3.0274,	labelID: n06359193 916
Priority map
0.7160070420891056
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
fan    545
object_array_displays_fixation_data/search_displays/present_044.png
Loading image
(893, 1428, 3)
Eye_movement  1
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: cleaver, meat cleaver, chopper; confidence:3.5071,	labelID: n03041632 499
1	 category: envelope; confidence:3.3408,	labelID: n03291819 549
2	 category: can opener, tin opener; confidence:3.0919,	labelID: n02951585 473
3	 category: corkscrew, bottle screw; confidence:3.0105,	labelID: n03109150 512
4	 category: face powder; confidence:2.9736,	labelID: n03314780 551
Priority map
0.790916495516594
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  2
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: pencil sharpener; confidence:7.2269,	labelID: n03908714 710
1	 category: nipple; confidence:6.4466,	labelID: n03825788 680
2	 category: whistle; confidence:6.3358,	labelID: n04579432 902
3	 category: candle, taper, wax light; confidence:6.1642,	labelID: n02948072 470
4	 category: pill bottle; confidence:5.7883,	labelID: n03937543 720
Priority map
0.8516394370271277
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  3
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: teddy, teddy bear; confidence:8.5691,	labelID: n04399382 850
1	 category: necklace; confidence:7.9867,	labelID: n03814906 679
2	 category: bib; confidence:6.5347,	labelID: n02834397 443
3	 category: knot; confidence:6.1606,	labelID: n03627232 616
4	 category: hair slide; confidence:6.1571,	labelID: n03476684 584
Priority map
0.9029156114141236
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  4
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: necklace; confidence:15.4485,	labelID: n03814906 679
1	 category: hair slide; confidence:14.7174,	labelID: n03476684 584
2	 category: chain; confidence:12.5480,	labelID: n02999410 488
3	 category: letter opener, paper knife, paperknife; confidence:10.6022,	labelID: n03658185 623
4	 category: bottlecap; confidence:10.2234,	labelID: n02877765 455
Priority map
0.7461452040643192
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  5
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: stretcher; confidence:5.8548,	labelID: n04336792 830
1	 category: tricycle, trike, velocipede; confidence:5.8359,	labelID: n04482393 870
2	 category: toyshop; confidence:5.5375,	labelID: n04462240 865
3	 category: pencil sharpener; confidence:5.0796,	labelID: n03908714 710
4	 category: drilling platform, offshore rig; confidence:5.0556,	labelID: n03240683 540
Priority map
0.5275946907221437
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  6
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: French loaf; confidence:15.5510,	labelID: n07684084 930
1	 category: toaster; confidence:11.4866,	labelID: n04442312 859
2	 category: rubber eraser, rubber, pencil eraser; confidence:9.2474,	labelID: n04116512 767
3	 category: buckeye, horse chestnut, conker; confidence:9.0857,	labelID: n12768682 990
4	 category: clog, geta, patten, sabot; confidence:9.0678,	labelID: n03047690 502
Priority map
0.634013464925132
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  7
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: bison; confidence:11.8469,	labelID: n02410509 347
1	 category: wild boar, boar, Sus scrofa; confidence:9.5203,	labelID: n02396427 342
2	 category: ibex, Capra ibex; confidence:9.1659,	labelID: n02417914 350
3	 category: hog, pig, grunter, squealer, Sus scrofa; confidence:9.1564,	labelID: n02395406 341
4	 category: warthog; confidence:8.6250,	labelID: n02397096 343
Priority map
0.7676500108169175
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  8
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: water bottle; confidence:11.9765,	labelID: n04557648 898
1	 category: lighter, light, igniter, ignitor; confidence:10.4003,	labelID: n03666591 626
2	 category: water jug; confidence:8.7709,	labelID: n04560804 899
3	 category: oil filter; confidence:8.6749,	labelID: n03843555 686
4	 category: barrel, cask; confidence:8.4883,	labelID: n02795169 427
Priority map
0.9331665079519567
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  9
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: leafhopper; confidence:9.9678,	labelID: n02259212 317
1	 category: tick; confidence:9.2980,	labelID: n01776313 78
2	 category: cockroach, roach; confidence:8.2779,	labelID: n02233338 314
3	 category: isopod; confidence:7.4739,	labelID: n01990800 126
4	 category: rhinoceros beetle; confidence:7.1894,	labelID: n02174001 306
Priority map
0.9354552123745292
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  10
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: Polaroid camera, Polaroid Land camera; confidence:9.1464,	labelID: n03976467 732
1	 category: scale, weighing machine; confidence:8.7864,	labelID: n04141975 778
2	 category: cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM; confidence:8.7787,	labelID: n02977058 480
3	 category: digital watch; confidence:8.3238,	labelID: n03197337 531
4	 category: safe; confidence:8.2229,	labelID: n04125021 771
Priority map
0.9232723525038035
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  11
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: electric fan, blower; confidence:11.0283,	labelID: n03271574 545
1	 category: solar dish, solar collector, solar furnace; confidence:9.3570,	labelID: n04258138 807
2	 category: spotlight, spot; confidence:8.6168,	labelID: n04286575 818
3	 category: strainer; confidence:7.9164,	labelID: n04332243 828
4	 category: parachute, chute; confidence:7.5938,	labelID: n03888257 701
socks    806
object_array_displays_fixation_data/search_displays/present_045.png
Loading image
(893, 1428, 3)
Eye_movement  1
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: cleaver, meat cleaver, chopper; confidence:3.5071,	labelID: n03041632 499
1	 category: envelope; confidence:3.3408,	labelID: n03291819 549
2	 category: can opener, tin opener; confidence:3.0919,	labelID: n02951585 473
3	 category: corkscrew, bottle screw; confidence:3.0105,	labelID: n03109150 512
4	 category: face powder; confidence:2.9736,	labelID: n03314780 551
Priority map
0.9882356160258718
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  2
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: microphone, mike; confidence:10.5363,	labelID: n03759954 650
1	 category: power drill; confidence:9.8845,	labelID: n03995372 740
2	 category: spotlight, spot; confidence:9.2559,	labelID: n04286575 818
3	 category: thimble; confidence:9.1393,	labelID: n04423845 855
4	 category: whistle; confidence:8.9918,	labelID: n04579432 902
Priority map
0.9109108901471754
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  3
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: hair slide; confidence:8.1446,	labelID: n03476684 584
1	 category: buckle; confidence:8.1057,	labelID: n02910353 464
2	 category: necklace; confidence:7.0173,	labelID: n03814906 679
3	 category: lighter, light, igniter, ignitor; confidence:6.9553,	labelID: n03666591 626
4	 category: safety pin; confidence:6.3867,	labelID: n04127249 772
Priority map
0.7453290565374558
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  4
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: birdhouse; confidence:7.0387,	labelID: n02843684 448
1	 category: desk; confidence:6.7746,	labelID: n03179701 526
2	 category: guillotine; confidence:6.5279,	labelID: n03467068 583
3	 category: grand piano, grand; confidence:5.8721,	labelID: n03452741 579
4	 category: hook, claw; confidence:5.7531,	labelID: n03532672 600
Priority map
0.956393158480692
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  5
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: hair slide; confidence:8.2781,	labelID: n03476684 584
1	 category: can opener, tin opener; confidence:7.8137,	labelID: n02951585 473
2	 category: whistle; confidence:7.8086,	labelID: n04579432 902
3	 category: maraca; confidence:7.0395,	labelID: n03720891 641
4	 category: screwdriver; confidence:6.5328,	labelID: n04154565 784
Priority map
0.564677693480772
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  6
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: hair slide; confidence:6.1267,	labelID: n03476684 584
1	 category: ant, emmet, pismire; confidence:5.0515,	labelID: n02219486 310
2	 category: stretcher; confidence:4.9898,	labelID: n04336792 830
3	 category: warplane, military plane; confidence:4.3644,	labelID: n04552348 895
4	 category: rifle; confidence:4.3451,	labelID: n04090263 764
Priority map
0.6567188662550979
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  7
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: loupe, jeweler's loupe; confidence:8.1466,	labelID: n03692522 633
1	 category: lens cap, lens cover; confidence:8.0857,	labelID: n03657121 622
2	 category: reflex camera; confidence:7.7452,	labelID: n04069434 759
3	 category: joystick; confidence:7.1661,	labelID: n03602883 613
4	 category: water bottle; confidence:7.1068,	labelID: n04557648 898
Priority map
0.8774832358597165
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  8
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: bald eagle, American eagle, Haliaeetus leucocephalus; confidence:7.1181,	labelID: n01614925 22
1	 category: fiddler crab; confidence:6.7790,	labelID: n01980166 120
2	 category: weevil; confidence:6.2085,	labelID: n02177972 307
3	 category: honeycomb; confidence:5.7503,	labelID: n03530642 599
4	 category: rock crab, Cancer irroratus; confidence:5.7113,	labelID: n01978455 119
Priority map
0.9555140469171596
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  9
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: hook, claw; confidence:6.6073,	labelID: n03532672 600
1	 category: hair slide; confidence:5.8666,	labelID: n03476684 584
2	 category: altar; confidence:5.7393,	labelID: n02699494 406
3	 category: wall clock; confidence:5.6610,	labelID: n04548280 892
4	 category: swing; confidence:5.4691,	labelID: n04371774 843
Priority map
0.6144228634214488
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  10
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: pencil sharpener; confidence:7.4984,	labelID: n03908714 710
1	 category: envelope; confidence:7.3552,	labelID: n03291819 549
2	 category: sewing machine; confidence:7.1119,	labelID: n04179913 786
3	 category: switch, electric switch, electrical switch; confidence:6.7382,	labelID: n04372370 844
4	 category: safety pin; confidence:6.6882,	labelID: n04127249 772
Priority map
0.8399467726760699
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  11
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: broom; confidence:7.9045,	labelID: n02906734 462
1	 category: knot; confidence:6.7895,	labelID: n03627232 616
2	 category: swab, swob, mop; confidence:6.4150,	labelID: n04367480 840
3	 category: maypole; confidence:6.1907,	labelID: n03733131 645
4	 category: hook, claw; confidence:6.0340,	labelID: n03532672 600
Priority map
0.3668903764816046
crib    520
object_array_displays_fixation_data/search_displays/present_046.png
Loading image
(893, 1428, 3)
Eye_movement  1
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: cleaver, meat cleaver, chopper; confidence:3.5071,	labelID: n03041632 499
1	 category: envelope; confidence:3.3408,	labelID: n03291819 549
2	 category: can opener, tin opener; confidence:3.0919,	labelID: n02951585 473
3	 category: corkscrew, bottle screw; confidence:3.0105,	labelID: n03109150 512
4	 category: face powder; confidence:2.9736,	labelID: n03314780 551
Priority map
0.9931279281651945
Eye_movement  2
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: pot, flowerpot; confidence:7.2224,	labelID: n03991062 738
1	 category: mortar; confidence:6.9662,	labelID: n03786901 666
2	 category: cup; confidence:6.8340,	labelID: n07930864 968
3	 category: vase; confidence:6.8190,	labelID: n04522168 883
4	 category: piggy bank, penny bank; confidence:6.2970,	labelID: n03935335 719
Priority map
0.9878507727617936
Eye_movement  3
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: cradle; confidence:9.6520,	labelID: n03125729 516
1	 category: plate rack; confidence:9.6408,	labelID: n03961711 729
2	 category: crib, cot; confidence:9.5591,	labelID: n03131574 520
3	 category: bassinet; confidence:7.9606,	labelID: n02804414 431
4	 category: hook, claw; confidence:6.8060,	labelID: n03532672 600
socks    806
object_array_displays_fixation_data/search_displays/present_053.png
Loading image
(893, 1428, 3)
Eye_movement  1
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: cleaver, meat cleaver, chopper; confidence:3.5071,	labelID: n03041632 499
1	 category: envelope; confidence:3.3408,	labelID: n03291819 549
2	 category: can opener, tin opener; confidence:3.0919,	labelID: n02951585 473
3	 category: corkscrew, bottle screw; confidence:3.0105,	labelID: n03109150 512
4	 category: face powder; confidence:2.9736,	labelID: n03314780 551
Priority map
0.9010269001677513
Eye_movement  2
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: hair slide; confidence:12.5473,	labelID: n03476684 584
1	 category: necklace; confidence:10.4508,	labelID: n03814906 679
2	 category: bolo tie, bolo, bola tie, bola; confidence:8.4835,	labelID: n02865351 451
3	 category: bottlecap; confidence:8.4074,	labelID: n02877765 455
4	 category: buckle; confidence:7.6463,	labelID: n02910353 464
Priority map
0.7649664398079824
Eye_movement  3
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: Christmas stocking; confidence:8.3149,	labelID: n03026506 496
1	 category: sock; confidence:8.0486,	labelID: n04254777 806
2	 category: diaper, nappy, napkin; confidence:7.1414,	labelID: n03188531 529
3	 category: mitten; confidence:6.8238,	labelID: n03775071 658
4	 category: hair slide; confidence:6.5753,	labelID: n03476684 584
tent    672
object_array_displays_fixation_data/search_displays/present_060.png
Loading image
(893, 1428, 3)
Eye_movement  1
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: cleaver, meat cleaver, chopper; confidence:3.5071,	labelID: n03041632 499
1	 category: envelope; confidence:3.3408,	labelID: n03291819 549
2	 category: can opener, tin opener; confidence:3.0919,	labelID: n02951585 473
3	 category: corkscrew, bottle screw; confidence:3.0105,	labelID: n03109150 512
4	 category: face powder; confidence:2.9736,	labelID: n03314780 551
Priority map
1.0
Eye_movement  2
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: parachute, chute; confidence:7.4632,	labelID: n03888257 701
1	 category: mountain tent; confidence:6.8645,	labelID: n03792972 672
2	 category: umbrella; confidence:6.7546,	labelID: n04507155 879
3	 category: unicycle, monocycle; confidence:5.7806,	labelID: n04509417 880
4	 category: balloon; confidence:5.2605,	labelID: n02782093 417
fan    545
object_array_displays_fixation_data/search_displays/present_062.png
Loading image
(893, 1428, 3)
Eye_movement  1
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: cleaver, meat cleaver, chopper; confidence:3.5071,	labelID: n03041632 499
1	 category: envelope; confidence:3.3408,	labelID: n03291819 549
2	 category: can opener, tin opener; confidence:3.0919,	labelID: n02951585 473
3	 category: corkscrew, bottle screw; confidence:3.0105,	labelID: n03109150 512
4	 category: face powder; confidence:2.9736,	labelID: n03314780 551
Priority map
1.0
Eye_movement  2
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: electric fan, blower; confidence:13.0919,	labelID: n03271574 545
1	 category: spotlight, spot; confidence:11.8366,	labelID: n04286575 818
2	 category: space heater; confidence:7.9246,	labelID: n04265275 811
3	 category: tennis ball; confidence:7.8880,	labelID: n04409515 852
4	 category: strainer; confidence:7.6261,	labelID: n04332243 828
clock    409
object_array_displays_fixation_data/search_displays/present_066.png
Loading image
(893, 1428, 3)
Eye_movement  1
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: cleaver, meat cleaver, chopper; confidence:3.5071,	labelID: n03041632 499
1	 category: envelope; confidence:3.3408,	labelID: n03291819 549
2	 category: can opener, tin opener; confidence:3.0919,	labelID: n02951585 473
3	 category: corkscrew, bottle screw; confidence:3.0105,	labelID: n03109150 512
4	 category: face powder; confidence:2.9736,	labelID: n03314780 551
Priority map
1.0
Eye_movement  2
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: wall clock; confidence:11.8316,	labelID: n04548280 892
1	 category: analog clock; confidence:11.6271,	labelID: n02708093 409
2	 category: barometer; confidence:10.2892,	labelID: n02794156 426
3	 category: magnetic compass; confidence:7.9653,	labelID: n03706229 635
4	 category: stopwatch, stop watch; confidence:7.9133,	labelID: n04328186 826
garbage_can    412
object_array_displays_fixation_data/search_displays/present_070.png
Loading image
(893, 1428, 3)
Eye_movement  1
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: cleaver, meat cleaver, chopper; confidence:3.5071,	labelID: n03041632 499
1	 category: envelope; confidence:3.3408,	labelID: n03291819 549
2	 category: can opener, tin opener; confidence:3.0919,	labelID: n02951585 473
3	 category: corkscrew, bottle screw; confidence:3.0105,	labelID: n03109150 512
4	 category: face powder; confidence:2.9736,	labelID: n03314780 551
Priority map
1.0
Eye_movement  2
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: saltshaker, salt shaker; confidence:8.4070,	labelID: n04131690 773
1	 category: coffeepot; confidence:7.5055,	labelID: n03063689 505
2	 category: milk can; confidence:6.9892,	labelID: n03764736 653
3	 category: water jug; confidence:6.9359,	labelID: n04560804 899
4	 category: pitcher, ewer; confidence:6.7112,	labelID: n03950228 725
Priority map
0.753754019918438
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  3
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin; confidence:7.9935,	labelID: n02747177 412
1	 category: soap dispenser; confidence:7.1702,	labelID: n04254120 804
2	 category: pencil sharpener; confidence:6.6808,	labelID: n03908714 710
3	 category: lighter, light, igniter, ignitor; confidence:6.1694,	labelID: n03666591 626
4	 category: bucket, pail; confidence:6.0892,	labelID: n02909870 463
garbage_can    412
object_array_displays_fixation_data/search_displays/present_071.png
Loading image
(893, 1428, 3)
Eye_movement  1
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: cleaver, meat cleaver, chopper; confidence:3.5071,	labelID: n03041632 499
1	 category: envelope; confidence:3.3408,	labelID: n03291819 549
2	 category: can opener, tin opener; confidence:3.0919,	labelID: n02951585 473
3	 category: corkscrew, bottle screw; confidence:3.0105,	labelID: n03109150 512
4	 category: face powder; confidence:2.9736,	labelID: n03314780 551
Priority map
0.9990691113782126
Eye_movement  2
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: table lamp; confidence:8.1080,	labelID: n04380533 846
1	 category: lampshade, lamp shade; confidence:6.7225,	labelID: n03637318 619
2	 category: soap dispenser; confidence:6.5481,	labelID: n04254120 804
3	 category: spotlight, spot; confidence:5.5456,	labelID: n04286575 818
4	 category: nipple; confidence:4.7325,	labelID: n03825788 680
Priority map
0.8048480410482111
Eye_movement  3
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: orange; confidence:5.2669,	labelID: n07747607 950
1	 category: necklace; confidence:4.3380,	labelID: n03814906 679
2	 category: whistle; confidence:4.1691,	labelID: n04579432 902
3	 category: tricycle, trike, velocipede; confidence:4.1291,	labelID: n04482393 870
4	 category: maraca; confidence:4.1033,	labelID: n03720891 641
Priority map
0.9892572563084422
Eye_movement  4
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: bucket, pail; confidence:8.9455,	labelID: n02909870 463
1	 category: hamper; confidence:8.1833,	labelID: n03482405 588
2	 category: ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin; confidence:7.7102,	labelID: n02747177 412
3	 category: barrel, cask; confidence:7.5739,	labelID: n02795169 427
4	 category: strainer; confidence:6.7070,	labelID: n04332243 828
fan    545
object_array_displays_fixation_data/search_displays/present_072.png
Loading image
(893, 1428, 3)
Eye_movement  1
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: cleaver, meat cleaver, chopper; confidence:3.5071,	labelID: n03041632 499
1	 category: envelope; confidence:3.3408,	labelID: n03291819 549
2	 category: can opener, tin opener; confidence:3.0919,	labelID: n02951585 473
3	 category: corkscrew, bottle screw; confidence:3.0105,	labelID: n03109150 512
4	 category: face powder; confidence:2.9736,	labelID: n03314780 551
Priority map
0.9177775828891825
Eye_movement  2
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: necklace; confidence:6.7114,	labelID: n03814906 679
1	 category: hamper; confidence:6.5220,	labelID: n03482405 588
2	 category: knot; confidence:6.3351,	labelID: n03627232 616
3	 category: panpipe, pandean pipe, syrinx; confidence:6.2121,	labelID: n03884397 699
4	 category: swing; confidence:6.1771,	labelID: n04371774 843
Priority map
0.8932111162963506
Eye_movement  3
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: pitcher, ewer; confidence:10.7139,	labelID: n03950228 725
1	 category: water jug; confidence:9.2831,	labelID: n04560804 899
2	 category: vase; confidence:8.9041,	labelID: n04522168 883
3	 category: nipple; confidence:8.6349,	labelID: n03825788 680
4	 category: whiskey jug; confidence:8.1959,	labelID: n04579145 901
Priority map
0.9516401815645879
Eye_movement  4
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: shield, buckler; confidence:10.7150,	labelID: n04192698 787
1	 category: wall clock; confidence:10.5261,	labelID: n04548280 892
2	 category: analog clock; confidence:8.8973,	labelID: n02708093 409
3	 category: bell cote, bell cot; confidence:7.5216,	labelID: n02825657 442
4	 category: barometer; confidence:7.1524,	labelID: n02794156 426
Priority map
0.7138593911658716
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  5
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: mousetrap; confidence:9.0517,	labelID: n03794056 674
1	 category: hamper; confidence:8.1666,	labelID: n03482405 588
2	 category: carton; confidence:7.9703,	labelID: n02971356 478
3	 category: binder, ring-binder; confidence:7.6697,	labelID: n02840245 446
4	 category: wallet, billfold, notecase, pocketbook; confidence:7.6467,	labelID: n04548362 893
Priority map
0.8455239309381961
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  6
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: tick; confidence:9.6342,	labelID: n01776313 78
1	 category: weevil; confidence:9.5205,	labelID: n02177972 307
2	 category: rhinoceros beetle; confidence:8.7508,	labelID: n02174001 306
3	 category: barn spider, Araneus cavaticus; confidence:8.6882,	labelID: n01773549 73
4	 category: long-horned beetle, longicorn, longicorn beetle; confidence:8.1430,	labelID: n02168699 303
Priority map
0.8488454626287028
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  7
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: magnetic compass; confidence:9.8783,	labelID: n03706229 635
1	 category: stopwatch, stop watch; confidence:8.0973,	labelID: n04328186 826
2	 category: analog clock; confidence:7.9852,	labelID: n02708093 409
3	 category: digital watch; confidence:7.2409,	labelID: n03197337 531
4	 category: tractor; confidence:6.9242,	labelID: n04465501 866
Priority map
1.0
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  8
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: magnetic compass; confidence:7.6269,	labelID: n03706229 635
1	 category: crane; confidence:6.9829,	labelID: n03126707 517
2	 category: reel; confidence:6.6494,	labelID: n04067472 758
3	 category: waffle iron; confidence:6.4766,	labelID: n04542943 891
4	 category: scale, weighing machine; confidence:6.4556,	labelID: n04141975 778
Priority map
0.9307986382243438
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  9
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: magnetic compass; confidence:10.3725,	labelID: n03706229 635
1	 category: analog clock; confidence:9.8719,	labelID: n02708093 409
2	 category: electric fan, blower; confidence:9.6468,	labelID: n03271574 545
3	 category: barometer; confidence:8.3149,	labelID: n02794156 426
4	 category: wall clock; confidence:8.2442,	labelID: n04548280 892
microwave_oven    651
object_array_displays_fixation_data/search_displays/present_073.png
Loading image
(893, 1428, 3)
Eye_movement  1
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: cleaver, meat cleaver, chopper; confidence:3.5071,	labelID: n03041632 499
1	 category: envelope; confidence:3.3408,	labelID: n03291819 549
2	 category: can opener, tin opener; confidence:3.0919,	labelID: n02951585 473
3	 category: corkscrew, bottle screw; confidence:3.0105,	labelID: n03109150 512
4	 category: face powder; confidence:2.9736,	labelID: n03314780 551
Priority map
0.9902114276478768
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  2
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: remote control, remote; confidence:8.6429,	labelID: n04074963 761
1	 category: digital clock; confidence:7.5347,	labelID: n03196217 530
2	 category: envelope; confidence:6.6169,	labelID: n03291819 549
3	 category: jersey, T-shirt, tee shirt; confidence:6.4632,	labelID: n03595614 610
4	 category: analog clock; confidence:6.4308,	labelID: n02708093 409
Priority map
0.9856904952825448
Eye_movement  3
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: microwave, microwave oven; confidence:9.0552,	labelID: n03761084 651
1	 category: digital clock; confidence:8.0294,	labelID: n03196217 530
2	 category: stove; confidence:6.0778,	labelID: n04330267 827
3	 category: rotisserie; confidence:5.9988,	labelID: n04111531 766
4	 category: digital watch; confidence:5.5775,	labelID: n03197337 531
socks    806
object_array_displays_fixation_data/search_displays/present_074.png
Loading image
(893, 1428, 3)
Eye_movement  1
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: coucal; confidence:3.5997,	labelID: n01824575 91
1	 category: kite; confidence:3.3895,	labelID: n01608432 21
2	 category: ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle; confidence:3.3416,	labelID: n02165456 301
3	 category: chain saw, chainsaw; confidence:3.2899,	labelID: n03000684 491
4	 category: house finch, linnet, Carpodacus mexicanus; confidence:3.1900,	labelID: n01532829 12
Priority map
0.9755709388400744
Eye_movement  2
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: envelope; confidence:7.8953,	labelID: n03291819 549
1	 category: web site, website, internet site, site; confidence:6.6354,	labelID: n06359193 916
2	 category: hair slide; confidence:6.5270,	labelID: n03476684 584
3	 category: pencil sharpener; confidence:5.5865,	labelID: n03908714 710
4	 category: safety pin; confidence:5.2828,	labelID: n04127249 772
Priority map
0.9970245554371736
Eye_movement  3
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: bottlecap; confidence:6.5807,	labelID: n02877765 455
1	 category: acorn; confidence:6.5029,	labelID: n12267677 988
2	 category: necklace; confidence:6.0095,	labelID: n03814906 679
3	 category: lorikeet; confidence:6.0030,	labelID: n01820546 90
4	 category: magnetic compass; confidence:5.8579,	labelID: n03706229 635
Priority map
1.0
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  4
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: sock; confidence:7.0085,	labelID: n04254777 806
1	 category: Christmas stocking; confidence:6.2987,	labelID: n03026506 496
2	 category: bow tie, bow-tie, bowtie; confidence:5.5002,	labelID: n02883205 457
3	 category: wool, woolen, woollen; confidence:5.4654,	labelID: n04599235 911
4	 category: ski mask; confidence:5.4206,	labelID: n04229816 796
rug    741
object_array_displays_fixation_data/search_displays/present_075.png
Loading image
(893, 1428, 3)
Eye_movement  1
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: cleaver, meat cleaver, chopper; confidence:3.5071,	labelID: n03041632 499
1	 category: envelope; confidence:3.3408,	labelID: n03291819 549
2	 category: can opener, tin opener; confidence:3.0919,	labelID: n02951585 473
3	 category: corkscrew, bottle screw; confidence:3.0105,	labelID: n03109150 512
4	 category: face powder; confidence:2.9736,	labelID: n03314780 551
Priority map
0.9408057231992234
Eye_movement  2
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: magnetic compass; confidence:11.7392,	labelID: n03706229 635
1	 category: bottlecap; confidence:7.1805,	labelID: n02877765 455
2	 category: beer bottle; confidence:6.6293,	labelID: n02823428 440
3	 category: clog, geta, patten, sabot; confidence:6.4117,	labelID: n03047690 502
4	 category: loupe, jeweler's loupe; confidence:6.3120,	labelID: n03692522 633
Priority map
0.9496106264427909
Eye_movement  3
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: frying pan, frypan, skillet; confidence:12.0329,	labelID: n03400231 567
1	 category: mixing bowl; confidence:10.3136,	labelID: n03775546 659
2	 category: wok; confidence:10.1770,	labelID: n04596742 909
3	 category: soup bowl; confidence:9.6782,	labelID: n04263257 809
4	 category: strainer; confidence:9.1927,	labelID: n04332243 828
Priority map
0.9784549847622985
Eye_movement  4
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: swing; confidence:9.5651,	labelID: n04371774 843
1	 category: parachute, chute; confidence:8.2204,	labelID: n03888257 701
2	 category: balloon; confidence:8.1021,	labelID: n02782093 417
3	 category: neck brace; confidence:8.0474,	labelID: n03814639 678
4	 category: maraca; confidence:6.9078,	labelID: n03720891 641
Priority map
0.8160345739407067
Eye_movement  5
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: radio telescope, radio reflector; confidence:9.6695,	labelID: n04044716 755
1	 category: solar dish, solar collector, solar furnace; confidence:9.2803,	labelID: n04258138 807
2	 category: shopping basket; confidence:7.5448,	labelID: n04204238 790
3	 category: crane; confidence:7.5046,	labelID: n03126707 517
4	 category: shopping cart; confidence:7.2263,	labelID: n04204347 791
Priority map
0.8363868609335857
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  6
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: rhinoceros beetle; confidence:8.3376,	labelID: n02174001 306
1	 category: scorpion; confidence:7.6729,	labelID: n01770393 71
2	 category: weevil; confidence:7.4897,	labelID: n02177972 307
3	 category: long-horned beetle, longicorn, longicorn beetle; confidence:7.4063,	labelID: n02168699 303
4	 category: dung beetle; confidence:6.7187,	labelID: n02172182 305
Priority map
0.9420654107395567
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  7
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: hamper; confidence:7.7648,	labelID: n03482405 588
1	 category: doormat, welcome mat; confidence:7.5372,	labelID: n03223299 539
2	 category: cradle; confidence:6.2178,	labelID: n03125729 516
3	 category: honeycomb; confidence:6.1181,	labelID: n03530642 599
4	 category: Band Aid; confidence:5.9447,	labelID: n02786058 419
Priority map
0.6706752722084002
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  8
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: jack-o'-lantern; confidence:9.0456,	labelID: n03590841 607
1	 category: hook, claw; confidence:8.1189,	labelID: n03532672 600
2	 category: ocarina, sweet potato; confidence:7.9923,	labelID: n03840681 684
3	 category: mask; confidence:7.2483,	labelID: n03724870 643
4	 category: whistle; confidence:6.8894,	labelID: n04579432 902
Priority map
0.9330019949710104
Eye_movement  9
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: prayer rug, prayer mat; confidence:9.2762,	labelID: n03998194 741
1	 category: vestment; confidence:7.8647,	labelID: n04532106 887
2	 category: throne; confidence:7.5572,	labelID: n04429376 857
3	 category: Windsor tie; confidence:6.0515,	labelID: n04591157 906
4	 category: velvet; confidence:5.7325,	labelID: n04525038 885
microwave_oven    651
object_array_displays_fixation_data/search_displays/present_076.png
Loading image
(893, 1428, 3)
Eye_movement  1
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: cleaver, meat cleaver, chopper; confidence:3.5071,	labelID: n03041632 499
1	 category: envelope; confidence:3.3408,	labelID: n03291819 549
2	 category: can opener, tin opener; confidence:3.0919,	labelID: n02951585 473
3	 category: corkscrew, bottle screw; confidence:3.0105,	labelID: n03109150 512
4	 category: face powder; confidence:2.9736,	labelID: n03314780 551
Priority map
0.7514404541254103
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  2
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: power drill; confidence:10.3066,	labelID: n03995372 740
1	 category: radio, wireless; confidence:7.5252,	labelID: n04041544 754
2	 category: tape player; confidence:7.5212,	labelID: n04392985 848
3	 category: screwdriver; confidence:7.3508,	labelID: n04154565 784
4	 category: joystick; confidence:7.1219,	labelID: n03602883 613
Priority map
0.9118258796620408
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  3
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: microwave, microwave oven; confidence:9.8595,	labelID: n03761084 651
1	 category: digital clock; confidence:9.4497,	labelID: n03196217 530
2	 category: digital watch; confidence:7.7511,	labelID: n03197337 531
3	 category: radio, wireless; confidence:7.5798,	labelID: n04041544 754
4	 category: CD player; confidence:7.5115,	labelID: n02988304 485
teddy_bear    850
object_array_displays_fixation_data/search_displays/present_077.png
Loading image
(893, 1428, 3)
Eye_movement  1
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: microphone, mike; confidence:3.9170,	labelID: n03759954 650
1	 category: puck, hockey puck; confidence:3.7989,	labelID: n04019541 746
2	 category: corkscrew, bottle screw; confidence:3.7316,	labelID: n03109150 512
3	 category: hook, claw; confidence:3.4177,	labelID: n03532672 600
4	 category: can opener, tin opener; confidence:3.3612,	labelID: n02951585 473
Priority map
0.9296488357016159
Eye_movement  2
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: teddy, teddy bear; confidence:12.0755,	labelID: n04399382 850
1	 category: pill bottle; confidence:7.4376,	labelID: n03937543 720
2	 category: nipple; confidence:7.4194,	labelID: n03825788 680
3	 category: cup; confidence:7.3863,	labelID: n07930864 968
4	 category: stethoscope; confidence:7.3272,	labelID: n04317175 823
teddy_bear    850
object_array_displays_fixation_data/search_displays/present_079.png
Loading image
(893, 1428, 3)
Eye_movement  1
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: cleaver, meat cleaver, chopper; confidence:3.5071,	labelID: n03041632 499
1	 category: envelope; confidence:3.3408,	labelID: n03291819 549
2	 category: can opener, tin opener; confidence:3.0919,	labelID: n02951585 473
3	 category: corkscrew, bottle screw; confidence:3.0105,	labelID: n03109150 512
4	 category: face powder; confidence:2.9736,	labelID: n03314780 551
Priority map
0.9998831825783203
Eye_movement  2
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: teddy, teddy bear; confidence:12.5729,	labelID: n04399382 850
1	 category: hair slide; confidence:7.3201,	labelID: n03476684 584
2	 category: earthstar; confidence:6.9589,	labelID: n13044778 995
3	 category: toyshop; confidence:6.2655,	labelID: n04462240 865
4	 category: wool, woolen, woollen; confidence:6.1169,	labelID: n04599235 911
crib    520
object_array_displays_fixation_data/search_displays/present_080.png
Loading image
(893, 1428, 3)
Eye_movement  1
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: sax, saxophone; confidence:3.6565,	labelID: n04141076 776
1	 category: corkscrew, bottle screw; confidence:3.2337,	labelID: n03109150 512
2	 category: hook, claw; confidence:2.9990,	labelID: n03532672 600
3	 category: red wine; confidence:2.7356,	labelID: n07892512 966
4	 category: quill, quill pen; confidence:2.7271,	labelID: n04033901 749
Priority map
0.9578637064632028
Eye_movement  2
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: crib, cot; confidence:12.6952,	labelID: n03131574 520
1	 category: cradle; confidence:12.6126,	labelID: n03125729 516
2	 category: shopping basket; confidence:9.6918,	labelID: n04204238 790
3	 category: hamper; confidence:9.1507,	labelID: n03482405 588
4	 category: bassinet; confidence:8.4799,	labelID: n02804414 431
microwave_oven    651
object_array_displays_fixation_data/search_displays/present_085.png
Loading image
(893, 1428, 3)
Eye_movement  1
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: cleaver, meat cleaver, chopper; confidence:3.5071,	labelID: n03041632 499
1	 category: envelope; confidence:3.3408,	labelID: n03291819 549
2	 category: can opener, tin opener; confidence:3.0919,	labelID: n02951585 473
3	 category: corkscrew, bottle screw; confidence:3.0105,	labelID: n03109150 512
4	 category: face powder; confidence:2.9736,	labelID: n03314780 551
Priority map
0.9631310724692457
Eye_movement  2
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: safe; confidence:6.5837,	labelID: n04125021 771
1	 category: web site, website, internet site, site; confidence:6.4699,	labelID: n06359193 916
2	 category: dial telephone, dial phone; confidence:6.3355,	labelID: n03187595 528
3	 category: microwave, microwave oven; confidence:5.9870,	labelID: n03761084 651
4	 category: cassette player; confidence:5.5712,	labelID: n02979186 482
teddy_bear    850
object_array_displays_fixation_data/search_displays/present_088.png
Loading image
(893, 1428, 3)
Eye_movement  1
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: cleaver, meat cleaver, chopper; confidence:3.5071,	labelID: n03041632 499
1	 category: envelope; confidence:3.3408,	labelID: n03291819 549
2	 category: can opener, tin opener; confidence:3.0919,	labelID: n02951585 473
3	 category: corkscrew, bottle screw; confidence:3.0105,	labelID: n03109150 512
4	 category: face powder; confidence:2.9736,	labelID: n03314780 551
Priority map
1.0
Eye_movement  2
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: necklace; confidence:11.2257,	labelID: n03814906 679
1	 category: hair slide; confidence:10.7875,	labelID: n03476684 584
2	 category: buckle; confidence:8.4257,	labelID: n02910353 464
3	 category: whistle; confidence:7.5811,	labelID: n04579432 902
4	 category: chain; confidence:7.1782,	labelID: n02999410 488
Priority map
0.8934962110285187
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  3
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: whistle; confidence:9.5024,	labelID: n04579432 902
1	 category: hair slide; confidence:9.1453,	labelID: n03476684 584
2	 category: letter opener, paper knife, paperknife; confidence:8.1542,	labelID: n03658185 623
3	 category: necklace; confidence:8.0203,	labelID: n03814906 679
4	 category: strainer; confidence:7.8436,	labelID: n04332243 828
Priority map
0.830604448100306
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  4
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: switch, electric switch, electrical switch; confidence:10.4931,	labelID: n04372370 844
1	 category: crane; confidence:9.7825,	labelID: n03126707 517
2	 category: whistle; confidence:8.8717,	labelID: n04579432 902
3	 category: lighter, light, igniter, ignitor; confidence:8.7849,	labelID: n03666591 626
4	 category: reel; confidence:8.7386,	labelID: n04067472 758
Priority map
0.8870945553119295
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  5
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: teddy, teddy bear; confidence:10.2766,	labelID: n04399382 850
1	 category: diaper, nappy, napkin; confidence:7.9269,	labelID: n03188531 529
2	 category: rubber eraser, rubber, pencil eraser; confidence:7.0909,	labelID: n04116512 767
3	 category: nipple; confidence:6.9137,	labelID: n03825788 680
4	 category: Band Aid; confidence:6.7669,	labelID: n02786058 419
crib    520
object_array_displays_fixation_data/search_displays/present_093.png
Loading image
(893, 1428, 3)
Eye_movement  1
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: red wine; confidence:4.0366,	labelID: n07892512 966
1	 category: cleaver, meat cleaver, chopper; confidence:3.7518,	labelID: n03041632 499
2	 category: envelope; confidence:3.6253,	labelID: n03291819 549
3	 category: hook, claw; confidence:3.4662,	labelID: n03532672 600
4	 category: lipstick, lip rouge; confidence:3.4075,	labelID: n03676483 629
Priority map
0.9835594977596996
Eye_movement  2
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: harvester, reaper; confidence:9.1350,	labelID: n03496892 595
1	 category: hamper; confidence:8.0774,	labelID: n03482405 588
2	 category: snowplow, snowplough; confidence:7.9534,	labelID: n04252225 803
3	 category: tractor; confidence:7.6142,	labelID: n04465501 866
4	 category: crane; confidence:7.5150,	labelID: n03126707 517
Priority map
0.5647301745632849
Eye_movement  3
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: tractor; confidence:16.4987,	labelID: n04465501 866
1	 category: snowplow, snowplough; confidence:15.9816,	labelID: n04252225 803
2	 category: plow, plough; confidence:14.9970,	labelID: n03967562 730
3	 category: harvester, reaper; confidence:14.5546,	labelID: n03496892 595
4	 category: jeep, landrover; confidence:13.6802,	labelID: n03594945 609
Priority map
0.7429377687392575
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  4
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: radio telescope, radio reflector; confidence:10.9881,	labelID: n04044716 755
1	 category: solar dish, solar collector, solar furnace; confidence:9.0652,	labelID: n04258138 807
2	 category: lacewing, lacewing fly; confidence:8.9603,	labelID: n02264363 318
3	 category: strainer; confidence:8.8127,	labelID: n04332243 828
4	 category: sombrero; confidence:8.4685,	labelID: n04259630 808
Priority map
0.49124254830146497
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  5
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: nail; confidence:8.7384,	labelID: n03804744 677
1	 category: screw; confidence:8.2194,	labelID: n04153751 783
2	 category: hammer; confidence:7.4775,	labelID: n03481172 587
3	 category: whistle; confidence:7.3896,	labelID: n04579432 902
4	 category: hook, claw; confidence:7.0419,	labelID: n03532672 600
Priority map
0.43529256614143164
Eye_movement  6
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: vase; confidence:7.9947,	labelID: n04522168 883
1	 category: pot, flowerpot; confidence:7.2624,	labelID: n03991062 738
2	 category: hook, claw; confidence:6.1413,	labelID: n03532672 600
3	 category: necklace; confidence:5.2183,	labelID: n03814906 679
4	 category: swing; confidence:4.8044,	labelID: n04371774 843
Priority map
0.35769791924766203
clock    409
object_array_displays_fixation_data/search_displays/present_094.png
Loading image
(893, 1428, 3)
Eye_movement  1
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: cleaver, meat cleaver, chopper; confidence:3.5071,	labelID: n03041632 499
1	 category: envelope; confidence:3.3408,	labelID: n03291819 549
2	 category: can opener, tin opener; confidence:3.0919,	labelID: n02951585 473
3	 category: corkscrew, bottle screw; confidence:3.0105,	labelID: n03109150 512
4	 category: face powder; confidence:2.9736,	labelID: n03314780 551
Priority map
0.9843794612730691
Eye_movement  2
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: stopwatch, stop watch; confidence:9.9593,	labelID: n04328186 826
1	 category: analog clock; confidence:9.9266,	labelID: n02708093 409
2	 category: wall clock; confidence:9.4190,	labelID: n04548280 892
3	 category: magnetic compass; confidence:8.9669,	labelID: n03706229 635
4	 category: barometer; confidence:8.5104,	labelID: n02794156 426
rug    741
object_array_displays_fixation_data/search_displays/present_102.png
Loading image
(893, 1428, 3)
Eye_movement  1
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: cleaver, meat cleaver, chopper; confidence:3.5071,	labelID: n03041632 499
1	 category: envelope; confidence:3.3408,	labelID: n03291819 549
2	 category: can opener, tin opener; confidence:3.0919,	labelID: n02951585 473
3	 category: corkscrew, bottle screw; confidence:3.0105,	labelID: n03109150 512
4	 category: face powder; confidence:2.9736,	labelID: n03314780 551
Priority map
0.9456552640714267
Eye_movement  2
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: lacewing, lacewing fly; confidence:7.9596,	labelID: n02264363 318
1	 category: maraca; confidence:7.8552,	labelID: n03720891 641
2	 category: cockroach, roach; confidence:7.3970,	labelID: n02233338 314
3	 category: chiton, coat-of-mail shell, sea cradle, polyplacophore; confidence:7.0720,	labelID: n01955084 116
4	 category: tick; confidence:6.8494,	labelID: n01776313 78
Priority map
0.9389732411261923
Eye_movement  3
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: folding chair; confidence:11.8792,	labelID: n03376595 559
1	 category: stretcher; confidence:11.3123,	labelID: n04336792 830
2	 category: shopping cart; confidence:10.6652,	labelID: n04204347 791
3	 category: mousetrap; confidence:9.2193,	labelID: n03794056 674
4	 category: plate rack; confidence:8.4071,	labelID: n03961711 729
Priority map
1.0
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  4
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: table lamp; confidence:9.7161,	labelID: n04380533 846
1	 category: altar; confidence:9.3668,	labelID: n02699494 406
2	 category: lampshade, lamp shade; confidence:8.3330,	labelID: n03637318 619
3	 category: prayer rug, prayer mat; confidence:8.1805,	labelID: n03998194 741
4	 category: throne; confidence:8.0757,	labelID: n04429376 857
socks    806
object_array_displays_fixation_data/search_displays/present_105.png
Loading image
(893, 1428, 3)
Eye_movement  1
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: cleaver, meat cleaver, chopper; confidence:3.5071,	labelID: n03041632 499
1	 category: envelope; confidence:3.3408,	labelID: n03291819 549
2	 category: can opener, tin opener; confidence:3.0919,	labelID: n02951585 473
3	 category: corkscrew, bottle screw; confidence:3.0105,	labelID: n03109150 512
4	 category: face powder; confidence:2.9736,	labelID: n03314780 551
Priority map
0.9831535334403461
Eye_movement  2
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: abacus; confidence:8.7039,	labelID: n02666196 398
1	 category: hamper; confidence:7.7927,	labelID: n03482405 588
2	 category: computer keyboard, keypad; confidence:7.5225,	labelID: n03085013 508
3	 category: waffle iron; confidence:7.4819,	labelID: n04542943 891
4	 category: hair slide; confidence:7.4726,	labelID: n03476684 584
Priority map
0.990822069881129
Eye_movement  3
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: abacus; confidence:9.7721,	labelID: n02666196 398
1	 category: buckle; confidence:8.2763,	labelID: n02910353 464
2	 category: combination lock; confidence:8.1708,	labelID: n03075370 507
3	 category: chime, bell, gong; confidence:8.0829,	labelID: n03017168 494
4	 category: purse; confidence:7.7836,	labelID: n04026417 748
Priority map
0.9697277215043344
Eye_movement  4
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: clog, geta, patten, sabot; confidence:7.8705,	labelID: n03047690 502
1	 category: baseball; confidence:7.4591,	labelID: n02799071 429
2	 category: purse; confidence:7.2859,	labelID: n04026417 748
3	 category: sandal; confidence:7.1898,	labelID: n04133789 774
4	 category: maraca; confidence:6.6805,	labelID: n03720891 641
Priority map
0.9435854058342592
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  5
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: chocolate sauce, chocolate syrup; confidence:8.0555,	labelID: n07836838 960
1	 category: black and gold garden spider, Argiope aurantia; confidence:7.0667,	labelID: n01773157 72
2	 category: rhinoceros beetle; confidence:6.9760,	labelID: n02174001 306
3	 category: barn spider, Araneus cavaticus; confidence:6.9403,	labelID: n01773549 73
4	 category: garden spider, Aranea diademata; confidence:6.8806,	labelID: n01773797 74
Priority map
0.784760127846094
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  6
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: kimono; confidence:11.2831,	labelID: n03617480 614
1	 category: abaya; confidence:9.4016,	labelID: n02667093 399
2	 category: cloak; confidence:8.1087,	labelID: n03045698 501
3	 category: academic gown, academic robe, judge's robe; confidence:7.6683,	labelID: n02669723 400
4	 category: poncho; confidence:7.4586,	labelID: n03980874 735
Priority map
0.8848695088933658
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  7
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: barn spider, Araneus cavaticus; confidence:9.7949,	labelID: n01773549 73
1	 category: tick; confidence:8.1984,	labelID: n01776313 78
2	 category: honeycomb; confidence:7.8334,	labelID: n03530642 599
3	 category: garden spider, Aranea diademata; confidence:7.7527,	labelID: n01773797 74
4	 category: kite; confidence:7.1691,	labelID: n01608432 21
Priority map
0.9012152781997413
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  8
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: maraca; confidence:6.5079,	labelID: n03720891 641
1	 category: vase; confidence:6.3596,	labelID: n04522168 883
2	 category: cowboy boot; confidence:6.3174,	labelID: n03124043 514
3	 category: sock; confidence:5.8387,	labelID: n04254777 806
4	 category: mask; confidence:5.8189,	labelID: n03724870 643
tent    672
object_array_displays_fixation_data/search_displays/present_106.png
Loading image
(893, 1428, 3)
Eye_movement  1
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: cleaver, meat cleaver, chopper; confidence:3.5071,	labelID: n03041632 499
1	 category: envelope; confidence:3.3408,	labelID: n03291819 549
2	 category: can opener, tin opener; confidence:3.0919,	labelID: n02951585 473
3	 category: corkscrew, bottle screw; confidence:3.0105,	labelID: n03109150 512
4	 category: face powder; confidence:2.9736,	labelID: n03314780 551
Priority map
0.9903725879395265
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  2
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: pencil sharpener; confidence:8.4834,	labelID: n03908714 710
1	 category: mountain tent; confidence:6.4740,	labelID: n03792972 672
2	 category: mousetrap; confidence:6.4438,	labelID: n03794056 674
3	 category: mouse, computer mouse; confidence:6.3195,	labelID: n03793489 673
4	 category: wallet, billfold, notecase, pocketbook; confidence:6.3136,	labelID: n04548362 893
clock    409
object_array_displays_fixation_data/search_displays/present_107.png
Loading image
(893, 1428, 3)
Eye_movement  1
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: cleaver, meat cleaver, chopper; confidence:3.5071,	labelID: n03041632 499
1	 category: envelope; confidence:3.3408,	labelID: n03291819 549
2	 category: can opener, tin opener; confidence:3.0919,	labelID: n02951585 473
3	 category: corkscrew, bottle screw; confidence:3.0105,	labelID: n03109150 512
4	 category: face powder; confidence:2.9736,	labelID: n03314780 551
Priority map
0.9190986054747652
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  2
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: microphone, mike; confidence:7.5224,	labelID: n03759954 650
1	 category: barber chair; confidence:7.4806,	labelID: n02791124 423
2	 category: bucket, pail; confidence:7.4298,	labelID: n02909870 463
3	 category: hamper; confidence:7.2258,	labelID: n03482405 588
4	 category: thimble; confidence:7.1646,	labelID: n04423845 855
Priority map
0.9982020104448003
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  3
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: analog clock; confidence:14.0442,	labelID: n02708093 409
1	 category: wall clock; confidence:14.0050,	labelID: n04548280 892
2	 category: barometer; confidence:8.4628,	labelID: n02794156 426
3	 category: magnetic compass; confidence:7.7529,	labelID: n03706229 635
4	 category: stopwatch, stop watch; confidence:7.6849,	labelID: n04328186 826
fan    545
object_array_displays_fixation_data/search_displays/present_108.png
Loading image
(893, 1428, 3)
Eye_movement  1
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: cleaver, meat cleaver, chopper; confidence:3.5071,	labelID: n03041632 499
1	 category: envelope; confidence:3.3408,	labelID: n03291819 549
2	 category: can opener, tin opener; confidence:3.0919,	labelID: n02951585 473
3	 category: corkscrew, bottle screw; confidence:3.0105,	labelID: n03109150 512
4	 category: face powder; confidence:2.9736,	labelID: n03314780 551
Priority map
1.0
Eye_movement  2
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: wallet, billfold, notecase, pocketbook; confidence:11.2802,	labelID: n04548362 893
1	 category: pencil box, pencil case; confidence:8.9180,	labelID: n03908618 709
2	 category: purse; confidence:8.8422,	labelID: n04026417 748
3	 category: packet; confidence:8.3661,	labelID: n03871628 692
4	 category: pillow; confidence:8.0457,	labelID: n03938244 721
Priority map
0.9931730594210124
Eye_movement  3
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea; confidence:7.1151,	labelID: n01665541 34
1	 category: puffer, pufferfish, blowfish, globefish; confidence:6.9191,	labelID: n02655020 397
2	 category: ruffed grouse, partridge, Bonasa umbellus; confidence:5.8476,	labelID: n01797886 82
3	 category: American alligator, Alligator mississipiensis; confidence:5.7633,	labelID: n01698640 50
4	 category: kite; confidence:5.5250,	labelID: n01608432 21
Priority map
0.9846132792313798
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  4
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: airship, dirigible; confidence:9.0116,	labelID: n02692877 405
1	 category: warplane, military plane; confidence:7.9685,	labelID: n04552348 895
2	 category: spotlight, spot; confidence:7.8979,	labelID: n04286575 818
3	 category: airliner; confidence:6.4375,	labelID: n02690373 404
4	 category: wing; confidence:6.3957,	labelID: n04592741 908
Priority map
0.9994775891312714
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  5
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: shopping cart; confidence:14.1307,	labelID: n04204347 791
1	 category: shopping basket; confidence:12.2594,	labelID: n04204238 790
2	 category: mousetrap; confidence:12.0409,	labelID: n03794056 674
3	 category: plate rack; confidence:11.3641,	labelID: n03961711 729
4	 category: hamper; confidence:10.5480,	labelID: n03482405 588
Priority map
0.7622437177080675
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  6
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: balloon; confidence:9.3843,	labelID: n02782093 417
1	 category: parachute, chute; confidence:7.1916,	labelID: n03888257 701
2	 category: hair slide; confidence:6.4046,	labelID: n03476684 584
3	 category: piggy bank, penny bank; confidence:6.2277,	labelID: n03935335 719
4	 category: maraca; confidence:6.0596,	labelID: n03720891 641
Priority map
0.8273819309552674
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  7
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: whistle; confidence:7.6736,	labelID: n04579432 902
1	 category: hook, claw; confidence:7.0966,	labelID: n03532672 600
2	 category: power drill; confidence:6.3082,	labelID: n03995372 740
3	 category: spotlight, spot; confidence:5.8459,	labelID: n04286575 818
4	 category: swing; confidence:5.8037,	labelID: n04371774 843
Priority map
0.6561121286084413
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  8
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: bagel, beigel; confidence:9.0948,	labelID: n07693725 931
1	 category: pretzel; confidence:8.8453,	labelID: n07695742 932
2	 category: teapot; confidence:7.9145,	labelID: n04398044 849
3	 category: saltshaker, salt shaker; confidence:6.5934,	labelID: n04131690 773
4	 category: piggy bank, penny bank; confidence:6.3672,	labelID: n03935335 719
Priority map
0.8782166926563336
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  9
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: cup; confidence:11.3704,	labelID: n07930864 968
1	 category: mixing bowl; confidence:11.2276,	labelID: n03775546 659
2	 category: strainer; confidence:10.3007,	labelID: n04332243 828
3	 category: bucket, pail; confidence:10.1126,	labelID: n02909870 463
4	 category: mortar; confidence:8.8834,	labelID: n03786901 666
Priority map
0.8526038248485946
Eye_movement  10
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: chime, bell, gong; confidence:6.9427,	labelID: n03017168 494
1	 category: binder, ring-binder; confidence:6.6247,	labelID: n02840245 446
2	 category: gong, tam-tam; confidence:6.4038,	labelID: n03447721 577
3	 category: plate rack; confidence:6.2963,	labelID: n03961711 729
4	 category: panpipe, pandean pipe, syrinx; confidence:6.0891,	labelID: n03884397 699
Priority map
0.999922586741398
Eye_movement  11
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: pencil sharpener; confidence:7.5513,	labelID: n03908714 710
1	 category: rubber eraser, rubber, pencil eraser; confidence:7.4306,	labelID: n04116512 767
2	 category: purse; confidence:6.8129,	labelID: n04026417 748
3	 category: pencil box, pencil case; confidence:6.2945,	labelID: n03908618 709
4	 category: hook, claw; confidence:6.0756,	labelID: n03532672 600
Priority map
0.9812006291345013
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  12
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: scale, weighing machine; confidence:9.0243,	labelID: n04141975 778
1	 category: gong, tam-tam; confidence:6.9829,	labelID: n03447721 577
2	 category: chime, bell, gong; confidence:6.3510,	labelID: n03017168 494
3	 category: frying pan, frypan, skillet; confidence:5.9874,	labelID: n03400231 567
4	 category: can opener, tin opener; confidence:5.9136,	labelID: n02951585 473
Priority map
0.9469164229718768
Eye_movement  13
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: abacus; confidence:7.8127,	labelID: n02666196 398
1	 category: whistle; confidence:7.4644,	labelID: n04579432 902
2	 category: sax, saxophone; confidence:7.1004,	labelID: n04141076 776
3	 category: cornet, horn, trumpet, trump; confidence:6.6836,	labelID: n03110669 513
4	 category: mousetrap; confidence:6.4678,	labelID: n03794056 674
Priority map
0.8336785380009961
Eye_movement  14
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: scale, weighing machine; confidence:5.9509,	labelID: n04141975 778
1	 category: magnetic compass; confidence:5.3792,	labelID: n03706229 635
2	 category: strainer; confidence:5.2188,	labelID: n04332243 828
3	 category: electric fan, blower; confidence:4.9155,	labelID: n03271574 545
4	 category: sandal; confidence:4.8182,	labelID: n04133789 774
Priority map
0.8692110032128628
Eye_movement  15
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: waffle iron; confidence:9.4661,	labelID: n04542943 891
1	 category: toaster; confidence:7.3727,	labelID: n04442312 859
2	 category: French loaf; confidence:7.2528,	labelID: n07684084 930
3	 category: face powder; confidence:7.1983,	labelID: n03314780 551
4	 category: pretzel; confidence:7.0111,	labelID: n07695742 932
Priority map
0.8675667958844104
Eye_movement  16
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: maraca; confidence:9.2144,	labelID: n03720891 641
1	 category: gong, tam-tam; confidence:9.1330,	labelID: n03447721 577
2	 category: chime, bell, gong; confidence:8.0909,	labelID: n03017168 494
3	 category: snail; confidence:7.9355,	labelID: n01944390 113
4	 category: plunger, plumber's helper; confidence:6.5174,	labelID: n03970156 731
Priority map
0.8401721494041058
Eye_movement  17
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: chime, bell, gong; confidence:6.6678,	labelID: n03017168 494
1	 category: whistle; confidence:6.0270,	labelID: n04579432 902
2	 category: teapot; confidence:6.0011,	labelID: n04398044 849
3	 category: necklace; confidence:5.9931,	labelID: n03814906 679
4	 category: scale, weighing machine; confidence:5.8834,	labelID: n04141975 778
Priority map
0.864282254592863
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  18
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: hair slide; confidence:9.6138,	labelID: n03476684 584
1	 category: tick; confidence:8.9264,	labelID: n01776313 78
2	 category: barn spider, Araneus cavaticus; confidence:6.4341,	labelID: n01773549 73
3	 category: banded gecko; confidence:6.0141,	labelID: n01675722 38
4	 category: pick, plectrum, plectron; confidence:5.9600,	labelID: n03929660 714
Priority map
0.8271182724456648
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  19
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: water bottle; confidence:7.6634,	labelID: n04557648 898
1	 category: quill, quill pen; confidence:7.1659,	labelID: n04033901 749
2	 category: table lamp; confidence:7.1094,	labelID: n04380533 846
3	 category: switch, electric switch, electrical switch; confidence:6.7854,	labelID: n04372370 844
4	 category: stupa, tope; confidence:6.6942,	labelID: n04346328 832
Priority map
0.8609985911578016
tent    672
object_array_displays_fixation_data/search_displays/present_110.png
Loading image
(893, 1428, 3)
Eye_movement  1
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: cleaver, meat cleaver, chopper; confidence:3.5071,	labelID: n03041632 499
1	 category: envelope; confidence:3.3408,	labelID: n03291819 549
2	 category: can opener, tin opener; confidence:3.0919,	labelID: n02951585 473
3	 category: corkscrew, bottle screw; confidence:3.0105,	labelID: n03109150 512
4	 category: face powder; confidence:2.9736,	labelID: n03314780 551
Priority map
0.9121095426380279
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  2
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: table lamp; confidence:7.8380,	labelID: n04380533 846
1	 category: chime, bell, gong; confidence:7.5167,	labelID: n03017168 494
2	 category: pickelhaube; confidence:7.0036,	labelID: n03929855 715
3	 category: perfume, essence; confidence:6.6548,	labelID: n03916031 711
4	 category: lampshade, lamp shade; confidence:6.4931,	labelID: n03637318 619
Priority map
1.0
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  3
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: mountain tent; confidence:12.5291,	labelID: n03792972 672
1	 category: umbrella; confidence:11.5818,	labelID: n04507155 879
2	 category: parachute, chute; confidence:10.6388,	labelID: n03888257 701
3	 category: sunglasses, dark glasses, shades; confidence:6.0637,	labelID: n04356056 837
4	 category: dome; confidence:5.8783,	labelID: n03220513 538
clock    409
object_array_displays_fixation_data/search_displays/present_112.png
Loading image
(893, 1428, 3)
Eye_movement  1
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: cleaver, meat cleaver, chopper; confidence:3.5071,	labelID: n03041632 499
1	 category: envelope; confidence:3.3408,	labelID: n03291819 549
2	 category: can opener, tin opener; confidence:3.0919,	labelID: n02951585 473
3	 category: corkscrew, bottle screw; confidence:3.0105,	labelID: n03109150 512
4	 category: face powder; confidence:2.9736,	labelID: n03314780 551
Priority map
0.9288520120761337
Eye_movement  2
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: bucket, pail; confidence:9.4406,	labelID: n02909870 463
1	 category: ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin; confidence:7.8533,	labelID: n02747177 412
2	 category: hamper; confidence:7.7742,	labelID: n03482405 588
3	 category: cup; confidence:7.6068,	labelID: n07930864 968
4	 category: pencil sharpener; confidence:7.5425,	labelID: n03908714 710
Priority map
0.9855372559289757
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  3
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: pencil sharpener; confidence:7.0649,	labelID: n03908714 710
1	 category: mousetrap; confidence:6.8832,	labelID: n03794056 674
2	 category: panpipe, pandean pipe, syrinx; confidence:6.6986,	labelID: n03884397 699
3	 category: crane; confidence:6.0369,	labelID: n03126707 517
4	 category: switch, electric switch, electrical switch; confidence:5.9718,	labelID: n04372370 844
Priority map
1.0
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Eye_movement  4
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: analog clock; confidence:13.0492,	labelID: n02708093 409
1	 category: wall clock; confidence:10.4196,	labelID: n04548280 892
2	 category: digital clock; confidence:10.1822,	labelID: n03196217 530
3	 category: switch, electric switch, electrical switch; confidence:8.1006,	labelID: n04372370 844
4	 category: stopwatch, stop watch; confidence:7.9981,	labelID: n04328186 826
fan    545
object_array_displays_fixation_data/search_displays/present_118.png
Loading image
(893, 1428, 3)
Eye_movement  1
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: cleaver, meat cleaver, chopper; confidence:3.5071,	labelID: n03041632 499
1	 category: envelope; confidence:3.3408,	labelID: n03291819 549
2	 category: can opener, tin opener; confidence:3.0919,	labelID: n02951585 473
3	 category: corkscrew, bottle screw; confidence:3.0105,	labelID: n03109150 512
4	 category: face powder; confidence:2.9736,	labelID: n03314780 551
Priority map
0.9642335438471493
Eye_movement  2
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: electric fan, blower; confidence:7.0751,	labelID: n03271574 545
1	 category: scale, weighing machine; confidence:6.7210,	labelID: n04141975 778
2	 category: plate rack; confidence:6.1303,	labelID: n03961711 729
3	 category: magnetic compass; confidence:5.7241,	labelID: n03706229 635
4	 category: hook, claw; confidence:5.7197,	labelID: n03532672 600
rug    741
object_array_displays_fixation_data/search_displays/present_125.png
Loading image
(893, 1428, 3)
Eye_movement  1
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: cleaver, meat cleaver, chopper; confidence:3.5071,	labelID: n03041632 499
1	 category: envelope; confidence:3.3408,	labelID: n03291819 549
2	 category: can opener, tin opener; confidence:3.0919,	labelID: n02951585 473
3	 category: corkscrew, bottle screw; confidence:3.0105,	labelID: n03109150 512
4	 category: face powder; confidence:2.9736,	labelID: n03314780 551
Priority map
0.9308302241467377
Eye_movement  2
INFO:tensorflow:Restoring parameters from ./modelparams/vgg_16.ckpt
['Fixated area of the image and the classificatoin results']
0	 category: shield, buckler; confidence:7.2846,	labelID: n04192698 787
1	 category: necklace; confidence:7.0899,	labelID: n03814906 679
2	 category: lighter, light, igniter, ignitor; confidence:6.8757,	labelID: n03666591 626
3	 category: bolo tie, bolo, bola tie, bola; confidence:6.6722,	labelID: n02865351 451
4	 category: prayer rug, prayer mat; confidence:6.3019,	labelID: n03998194 741
In [5]:
target_presence
Out[5]:
array([1., 0., 1., 1., 1., 1., 1., 0., 1., 1., 1., 1., 0., 1., 0., 1., 0.,
       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
       0., 1., 1., 1., 1., 1., 0., 1., 1., 1., 1.])
In [7]:
1 - sum(target_presence)/45
Out[7]:
0.15555555555555556
In [8]:
sum(target_presence)
Out[8]:
38.0
In [ ]:
target_
In [ ]:
np.save('Deep-BCN_fixations_CCF_object_array_target_absent_response.npy',)